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Prediction of lymph node metastasis in gastric cancer patients with neural networks
ELSEVIER
CancerLetters109(1996)141-148
CANCER LETTERS
Prediction of lymph node metastasis in gastric cancer patients with neural networks Karin Drostea,*, Elfriede Bollschweiler”, Thomas Waschulzikb, Thorsten Schiitzb, Rolf Engelbrechtb, Keiichi Maruyamac, J. Riidiger Siewerta “Department of Surgery, Technische Universitiit Miinchen, Klinikum rechts der Isar, Miinchen, Germany bGSF-Forschungszentrum fiir Umwelt und Gesundheit GmbH, MEDIS-lnstitut fiir Medizinische Informatik und Systemforschung, Neuherberg, Germany ‘Department of Surgical Oncology, National Cancer Centre Hospital, Tokyo, Japan
Received25 July 1996;accepted10 August1996
Abstract Artificial neural networks are a kind of pattern classifiers, with growing acceptancein medical and biological research.We applied a single layer perceptronto data of 4302 patients from National Cancer Centre in Tokyo and comparedthe results to the Maruyama diagnostic system (MDS) and classic statistical analysis with logistic regression. While logistic regression reachedno sensitivity and a specificity of 1.00 in median, MDS had a sensitivity of 0.74 and a specificity of 0.75 in median. The perceptron reacheda median sensitivity of 0.83 and a median specificity of 0.71. Keywords:
Artificial neural networks; Feedforward neural network; Gastric cancer; Lymph node metastasis;Computerized
diagnosis
1. Introduction Staging in gastric cancer is of special interest for therapeutic decisions. While T-category and organ metastasis can be determined with high reliability by medical imaging techniques like ultrasonography or computer tomography, there is still no method for accurate preoperative prediction of lymph node metastasis (LNM). Computer-aided diagnostic systems may offer new possibilities to this problem. * Corresponding author.ChirurgischeKlinik undPoliklinik, Klinikum rechtsder Isar der TU Miinchen,IsmaningerStmsse22, Manchen,Germany.TI:~.:+49 89 41404733.
Professor Maruyama developed a diagnostic system (MDS) based on data from National Cancer Centre in Tokyo (NCC) that allows preoperative prediction of LNM and prognosis for patients with gastric cancer [I]. This computer-aided diagnostic system has been validated among others with data from the Technische Universitst Miinchen [2]. In order to improve the results for correct prediction of lymph node involvement we developed a diagnostic support system using artificial neural networks (ANN) for preoperative prediction of LNM. The aim of this study was to compare a neural network system for preoperative assessment of lymph node metastasis in gastric cancer with the MDS.
0304-3835/96/$12.00 0 1996ElsevierScienceIrelandLtd. All rightsreserved PII 0304-3835(96)04438-l
142
K. Droste et al. I Cancer Letters 109 (1996) 141-148
2. Material and methods 2.1. Neural networks ANN are results of research in informatics, especially in artificial intelligence. Neuroinformatics, an interdisciplinary field of research within informatics, mathematics, physiology and anatomy, has two aims. On the one hand it analyses the brain functions by simulation. Suitable models are built with basic information processing techniques. For this purpose neuroinformatics uses the knowledge of biological brain research. Structures like neurons, axons, dendrites and synapses are modelled in data structures. Processes, e.g. excitation forming and conduction of nerve pulses, are simulated by mathematical functions. On the other hand, neuroinformatics apply ANN in practice. ANN are tolerant of missing values or inconsisten-
ties in data which often occur in medical data acquisition. They are supplied with self-learning mechanisms which simulate learning proceedings of human brain in a more or less biologically plausible manner [3]. ANN are known for being able to recognize and generalize correlations and rules from casebased databases, which then are applied to other ‘unknown’ data. They are also suitable to solve problems like constitution of hypotheses or pattern recognition. For these reasons they may be an appropriate method to analyze medical data. We use a simple variant of feedforward neural networks: the perceptron (Perceptron simulation utility, F. Grothe, GSF, Neuherberg, Germany). Rosenblatt first described this kind of ANN [4]. It consists of two layers of so-called neurons, an input layer and an output layer (Fig. 1). In this case the output layer includes two neurons, representing, respectively, metastasis and no metastasis of the lymph node
utput la Yer
SYnaP ses
input la Yer
Fig. 1. Diagram of perceptron. Output layer presents neurons as indicators for metastasis of LNG 1 input layer demonstrates neurons for the variables ‘Borrmann classification’ and ‘depth of invasion’ (Bl, Borrmann classification type 1; B2, Borrmam~ classification type 2; B3, Bonmann classification type 3; mm, mucosa; sm. submucosa; pm, muscularis propria).
K. Droste et al. I Cancer Letters IO9 (1996) 141-148
143
the weights of their synapsesthe activities of the output neurons are calculated (processing). This is illustrated in Fig. 2. It is similar to procedures of information processing in brain: multiple synapses lead information from many other nerve cells to one neuron. Incoming impulses are added up and transformed into neuron activity, which is representedby the frequency of leading impulses through the axon to other neurons. Synapsescan be of excitatory or inhibitory kind; this is indicated in the positive or negative weights of the connections of ANN.
Aw,(t) = p*Gj(t)‘A,(t)
w -weight, t - time, i - input, j -output, p - rate of learning, A - activity,
tag - target value, net - actual value
2.2. Data and statistics
*At)
A 2550 -Jz> L Ul
threshold
IleA - state of the nehvd Il-1owe.rlimit,ul-upperlimit
Fig. 2. Formula for learning and processing of ANN. Learning: w, weight; t, time: i, input; j, output; ~1,rate of learning; A, activity; targ, target value; act, actual value.
group. Each variable, e.g. depth of invasion or location of tumor, is organized in a group of neuronsin the input layer. A neuron within such a group is responsible for one value of the variable, e.g. infiltration of submucosawithin the neurons for the variable depth of invasion. Furthermore, each neuron of the input layer is connected to every neuron of the output layer. These connections are called synapses.In the phase of training the synapses are supplied with weights, dependenton the strength of the connection and the predictivity (learning). Even negative weights are possible. The mathematical algorithm underlying the training procedureis a kind of supervisedlearning 151. According to the activity of the input neurons and
A data setwith 4302 recordsfrom NCC of the years 1969- 1988was at our disposal.This data set includes information about patients’ age and gender, size, site and depth of invasion of the tumor, Borrmann classification [6] and variables for the number of affected lymph nodes in each of the 17 lymph node groups (LNG, Table 1). Age of patients ranges from 20 to 90 years with a meanof 58.2 years; distribution of sex is male: female 2: 1. The maximal diameter of tumor rangesfrom 1 to 220 mm with a mean of 53.4 mm. The variable Borrmann classification includes 2.4% type 1 (non-ulcerated polypoid), 17.5%type 2 (ulcerated elevated with well-defined border), 31.6% type 3 (ulcerated with partially diffuse infiltrating border), 8.0% type 4 (difTable 1 Lymph node location according to the Japanese Research Society for Gastric Cancer (JRSGC) LNG- 1 LNG-2 LNG-3 LNG-4 LNG-5 LNG-6 LNG-7 LNG-8 LNG-9 LNG-10 LNG-11 LNG-12 LNG-13 LNG-14 LNG-I5 LNG-16 LNG-17
Cardia right Cardia left Lesser curvature Greater curvature Suprapyloric Subpyloric Along left gastric artery Common hepatic artery Coeliac axis Splenic hilus Splenic artery Hepatoduodenal ligament Retropancreatic Mesenteric root Along middle colic vein Along abdominal aorta Other location
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K. Droste et al. I Cancer Letters 109 (1996) 141-148
fusely invading) tumors and also 40.5% early cancer (Table 2). The depth of invasion was distributed as follows: 22.3% mucosa, 18.2% submucosa, 9.6% muscularis propria, 7.3% subserosa, 6.6% serosa suspected, 22.1% serosa definite and 13.9% neighboring organs (Table 2). The location of tumor was coded in two variables: one variable for the longitudinal and one for the circular location. Both variables have four digits in hierarchical order. First place takes the location of the main tumor mass, second place takes location with next smaller tumor mass and so on. Only the main location was used in this part of the study. The distribution of values for the longitudinal location was 19.3% in the upper third, 42.9% in the middle third and 37.8% in the lower third. The circular location was lesser curvature in 44.9%, greater curvature in 12.3%, anterior wall in 14.5%, posterior wall in 20.2% and circular in 8.1% (Table 3). Results of MDS and ANN were proved by statistical analysis with logistic regression (‘SPSS for Windows’, Release 5.0.2, SPSS Inc. 1989-1993). This method is comparable to our ANN because it regresses a dichotomous dependent variable (output) on a set of independent variables (input). Independent variables may be metric or categorical. The parameters of the model are estimated using the maxiTable 2 Distribution of Bonmann classification and depth of invasion o/o Borrmann classification Early cancer TYPO ’ Type 2 Type 3 Type 4
40.5 2.4 17.5 31.6 8.0
Depth of invasion Mucosa (mm) Submucosa (sm) Muscularis propria (pm>1 Subserosa (SS) Serosa suspected (Sl) Serosa definite (S2) Neighboring organs
22.3 18.2 9.6 7.3 6.6 22.1 13.9
Borrmarm classification: type 1, non-ulcerated polypoid; type 2, ulcerated elevated with well-defined border; type 3, ulcerated with partially diffuse infiltrating border; type 4, diffusely invading.
Table 3 Distribution of tumor location
Location longitudinal Upper third Middle third Lower third
19.3 42.9 37.8
Location circular Lesser curvature Greater curvature Anterior wall Posterior wall Circular
44.9 12.3 14.5 20.2 8.1
mum-likelihood method. Results are predictions or estimates of probability of occurrence of an event (affection or non-affection of one LNG). 3. Results At least three different methods of prediction of lymph node metastasis have been evaluated: ANN, MDS and logistic regression. Table 4 shows the comparison of sensitivity and specificity of each of this methods. Results are presented for each of the three compartments of LNG and standardized on all 17 LNG (Table 1). The number of LNG is according to the General Rules for the Gastric Cancer Study in Surgery and Pathology [7]. Logistic regression reaches very high specificity (median 1.00, mean 0.98), whereas the sensitivity is very low, only 0.26 in mean with a median of 0.00. If used for preoperative staging this could lead to systematic overstaging of N-category. Especially for the LNG 7- 17, for which an accurate prediction is required, there is no more sensitivity given. Output of the MDS is based on group means of patients with same background. Patients are grouped by the variables Borrmann classification, depth of invasion and main location and a probability value for affection of the LNG is calculated. Sensitivity and specificity were evaluated at various thresholds between probability values of 0.1% and 20%. Given a threshold of 7%, above that the LNG is classified as affected; errors in both directions are nearly similar. The median specificity is 0.75 and the median sensitivity is 0.74. Fig. 3 shows the receiver operator char-
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K. Droste et al. I Cancer Letters 109 (1996) 141-148 Table 4 Sensitivity and specificity of different methods of prediction of lymph node metastasis in patients with gastric cancer
LOGREG MDS ANN 1 ANN2 ANN3 ANN4
Sensitivity LNG 1-6
Specificity LNG 7-11
LNG 12-17
Median
LNG l-6
LNG 7-11
LNG 12-17
Median
0.27 0.93 0.9 1 0.83 0.56 0.87
0.00 0.75 0.92 0.87 0.47 0.88
0.00 0.07 0.86 0.77 0.54 0.70
0.00 0.74 0.91 0.84 0.57 0.83
0.96 0.51 0.54 0.68 0.76 0.71
1.00 0.62 0.41 0.66 0.83 0.66
1.oo 0.96 0.48 0.66 0.63 0.72
1.oo 0.75 0.5 1 0.68 0.69 0.71
Logistic regression: Borrmann classification, depth of invasion, main location (saturated model). MDS: threshold 7%. ANN I input = Borrmann classification; ANN 2 input = depth of invasion; ANN 3 input = man location; ANN 4 input = Borrmann classification, depth of invasion, main location.
acteristic curve (ROC curve) of MDS with additional points for ANN. The ROC curve is used to describe the accuracy of a. test over a range of cut-off points. Area under the graph is a scale for the overall accuracy of a test [8]. Results of ANN are demonstrated for different steps of the study.. First, the predictive value of different variables on L,NM was tested using only one variable as input for ANN. At least four variables demonstrated predictive importance for LNM: depth
of invasion, maximal diameter, Borrmann classification and location of the tumor. Other variables like age, sex or histology have low or no bearing on prediction of LNM. Using a combination of the same variables as the MDS a median sensitivity of 0.83 and a median specificity of 0.71 could be achieved.
4. Discussion LNM is one of the most important prognostic fac-
Fig. 3. ROC-curve of MDS. Squares, MDS; circles, ANN.
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tors in patients with gastric cancer [9-121. Staging of lymph node metastasis is important not only for the choice of operation techniques or postoperative tumor staging but also for accurate preoperative staging and therapy decision including conservative therapies. The accurate prediction of actual LNM may avoid overstaging of patients with gastric cancer and lead to more certainty in therapy decision. Until now there is no method available for correct preoperative staging of N-category in gastric cancer patients. Even the accuracy of N-staging by computer tomography or endoscopic ultrasonography, if not limited to regional LNG, is unsatisfactory [ 13- 151. Computer-aided diagnostic systems and expert systems are used by a minority of clinicians, but if tested in controlled clinical trials they may offer new possibilities to medical education and clinical practice [16,17]. For some applications in medicine ANN were developed, e.g. diagnosis of acute pulmonary embolism [ 181, early detection and diagnosis of cancer [19] or evaluation of the MONICA-study of chronic heart disease [20]. A review of the huge flexibility of ANN and their diagnostic accuracy in a wide variety of areas is illustrated in [21]. We applied ANN to pretherapeutic prediction of LNM in patients with gastric cancer in comparison to the MDS and statistical analysis with logistic regression. Statistical analysis with logistic regression would lead to systematic over-staging of N-category and therefore possibly to more aggressive therapy than necessary. Dependency of postoperative morbidity rate on extend of lymph node dissection is still in dispute as well [23-261. The postoperative complication rate may be decreased by limiting the extend of lymph node dissection [22], but this is possibly purchased by an avoidable relapse and a shorter survival time of the patient. Thus, it is required to keep the probability of error for the prediction ‘not affected’ as small as possible. That is the way logistic regression works; the error for the prediction ‘not affected’ is minimized under purchasing a high error rate for the prediction ‘affected’. Similar results could also be achieved with MDS by using an appropriate threshold. Implementing a priority-function to ANN-tools will offer this ability to ANN as well. Results of this study already demonstrate that ANN are a suitable method for preoperative prediction of lymph node metastasis in gastric cancer. In general
the single layer perceptrons applied to that, suit very well for less complex problems or constitution of hypotheses. First, insertion of a-priori knowledge about physiological relations or special orders within variables into generation of ANN was deliberately abandoned. Each variable was explored about its predictive value on lymph node assessment, by taking it as only input for the ANN. Results of this series of experiments prove that it is possible to discover predictive variables out of a medical data base by using ANN. We discovered four variables as most important for prediction of LNM: depth of invasion, maximal diameter, Borrmann classification and location of the tumor. The same variables, except maximal diameter, are used for LNM-prediction in MDS. Different combinations of the four input variables in ANN prove the model used in MDS to be well-founded. Both methods lead to valid results. Correlation of LNM with T-category, site and size of the tumor or depth of invasion is confirmed in other studies [27-291; additionally, we found the Borrmann classification a very important predictive variable for LNM. We continue this study in order to increase the expectancy of hitting for both the prediction of affected and not affected lymph node groups, because higher sensitivity and specificity may avoid pretherapeutic overstaging or understaging and lead to more certainty in therapeutic decision. There are miscellaneous chances to reach this goal. First the input of variables may be improved. In the original data set the location of tumor was coded in two variables, one variable for the longitudinal and one for the circular location. Both variables have four digits in rank order. First digit takes the location of the main tumor mass, second digit takes location with next smaller tumor mass and so on. Advancement of coding of the input values enabled us to take into account our knowledge about the content of variables, e.g. the rank order of locations by tumor mass or the relation between longitudinal and circular location. Modelling the input layer and expansion of the network structure within the meaning of a ‘pseudo multi layer perceptron’ offers more possibilities for data processing with ANN and consideration of additional information about lymph node metastasis. At least the algorjthm of the ANN may be improved in special aspects [30]. Considering a priority function permits the weighting of errors of prediction. This part
K. Droste et al. I Cancer Letters IO!? (1996) 141-148
of the study is not yet concluded but to some extent clear improvements could be achieved. Furthermore, if a sufficient database with data about computer tomography, sonography and endoscopic ultrasonography is available, these data could be integrated in the ANN. Additional possibilities to improve the prediction of lymph node metastasis are the hierarchical combination of networks or the connection to a neural knowledge base:, containing information about the lymph system and lymph outlet of the stomach or ways of lymph node metastasis forming in gastric cancer. Several ANN managing different problems could be integrated into an expert system and periodical training phases with actual patient data would guarantee actuality of the weights of the synapses by consideration of changes, e.g. of epidemiology. References [l] KampschiKr, G.H.M., Maruyama, K., van de Velde, C.J.H., Sasako, M., Kinoshita, T. and Okabayashi, K. (1989) Computer analysis in making preoperative decisions: a rational approach to lymph node dissection in gastric cancer patients, Br. J. Surg., 76, 905-908. [2] Bollschweiler, E., Bottcher, K., Holscher, A.H., Sasako, M., Kinoshita, T., Maruyama, K. and Siewert, J. (1992) Preoperative assessmem of lymph node metastasis in patients with gastric cancer: evaluation of the Maruyama computer program, Br. J. Surg., 79, 156-160. [3] von Neumann, J. (1958) The Computer and the Brain. Yale University Press, New Haven, pp. 66-82. [4] Rosenblatt, F. t 1958) The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., 65, 386-408. [5] Widrow, B. and Hoff, M.E. (1960) Adaptive switching circuits. IRE WESCON Convention Record, pp. 96-104. IRE, New York. [6] Borrmann, R. (1926) Geschwtilste des Magens. In: Handbuch der speziellen pathologischen Anatomie, pp. 864-871. Editors: F. Henke and 0. Lubarsch. Springer, Berlin. [7] Japanese Research Society for Gastric Cancer (1981) The general rules for the gastric cancer study in surgery and pathology. Jpn. J. Surg., 11, 127-145. [8] Fletcher, R.H., Fletcher, S.W. and Wagner, E.H. (1988) Diagnosis, In: Clinical Epidemiology, pp. 42-75. Editors: N. Collins, C. Eckhart and G.N. Chalew. Williams and Wilkins, Baltimore, MD. [9] Secco, G.B., Fardelli, R., Campora, E., Rovida, S., Beatini, M., Larghero, G., Testa, T. and Prior, C. (1992) Extension of lymph node dissection and survival in primary gastric cancer, Int. Surg, 77, 242-247. [IO] Bottcher, K., Becker, K., Busch, R., Rider, J.D. and Siewert, J.R (1992) Prognosefaktoren beim Magencorcinom, Ergeb-
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nisse einer uni- und multivariaten Analyse. Chirurg, 63, 656-661. [ll] Oertli, D., Herzog, U. and Tondelli, P. (1994) Prognostic Factors in resected stomach carcinoma, Schweiz. Med. Wochenschr., 124,945-952. [12] Keller, E., Stutzer, H., Heitmann, K., Bauer, P., Gebbensleben, B. and Rohde, H. (1994) Lymph node staging rn 872 patients with carcinoma of the stomach and the pre!sumed benefit of lymphadenectomy (German Stomach Cancer TNM Study Group), .I. Am. Coll. Surg., 178, 38-46. [13] Dittler, H.J. and Siewert, J.R. (1993) Role of endoscopic ultrasonography in gastric carcinoma, Endoscopy, 25, 162166. [ 141 Dancygier, H. and Nattermann, C. (1993) Endoscopic ultrasonography: indications and results in diseases of the oesophagus and stomach, Schweiz. Rundsch. Med. Prax., 83, :281-1287. [ 151 Ziegler, K., Sanft, C., Zimmer, T., Zeitz, M., Felsenberg, D., Stein, H., Germer, C., Deutschmann, C. and Riecken, E.O. ( 1993) Comparison of computed tomography, endosonography, and intraoperative assessment in TN staging of gastric carcinoma, Gut, 34, 604-610. [ 161 Ohmann, C. and Horstmann, 0. (1992) Formale Entscheiclungshilfen in der Gastroenterologie: Ergebnisse einer IJmfrage, Z. Gastroenterol., 30, 558-564. [17] Johnston, M.E., Langton, K.B., Haynes, R.B. and Mathieu, A. (1994) Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research, Ann. Intern. Med., 120, 13.5-142. [18] Tourassi, G.D.. Floyd, C.E., Sostmann, H.D. and Coleman, R.E. (1995) Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection, Radiology, 194, 889-893. [19] Rogers, S.K., Ruck, D.W. and Kabrisky, M. (1994) Artificial neural networks for early detection and diagnosis of cancer, Cancer Lett., 77, 79-83. [20] Guandt, K., Waschulzik, T., Lewis, M., Hdrmann, A., Engelbrecht, R. and Brauer, W. (1993) Evaluation of epidemiological data with neural networks, Proc. World Congr. Neural Networks, 1, 257-260. [21] Baxt, W. (1995) Application of artificial neural networks to clinical medicine, Lancet, 346, 1135-l 138. [22] Dent, D.M., Madden, M.V. and Price, S.K. (1988) Randomized comparison of Rl and R2 gastrectomy for gastric carcinoma, Br. J. Surg., 75, 110-112. [23] Gall, F.P. and Hermanek, P. (1993) Systematic extended lymph node dissection in curative therapy of stomach cancer, Chirurg, 64, 1024-1030. [24] Kirchner, R. (1992) Chirurgie des Magenkarzinoms: eine Bestandsaufnahme, Zentralbl. Chir., 117, 577-582. [25] Bone&, J.J., Songun, I., Hermans, J., Sasako, M., Welvaart, K., Plikker, J.T.M., van Elk, P., Obertop, H., Gouma, D.J., Taat, C.W., van Lanschot, J., Meyer, S., de Graaf, P.W., von Meyenfeld, M.F., Tilanus, H. and van de Velde, C.J.H. (1995) Randomised comparison of morbidity after Dl and D2 dissection for gastric cancer in 996 Dutch patients, Lancet, 345, 745-748.
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[26] Cuschieri, A., Fayers, P., Fielding, J., Craven, J., Bancewicz, .I., Joypaul, V. and Cook, P. (1996) Postoperative morbidity and mortality after Dl and D2 resections for gastric cancer: preliminary results of the MRC randomised controlled surgical trial, Lancet, 347,995999. [27] Anai, H., Machara, Y., Oshiio, T., Baba, H., Orita, H., Okuyama, T. and Sugimachi, K. (1993) Prognosis for patients with carcinoma in the middle third of the stomach, J. Surg. Oncol., 53, 204-207. [28] Wu, C.W., Hsieh, h4.J., Lo, S.S., Tsay, S.H., Lui, W.Y. and
Peng, F.K. (1994) Lymph node metastasis from carcinoma of the distal one-third of the stomach, Cancer, 73, 2059-2064. [29] Maehara, Y., Orita, H., Okuyama, T., Moriguchi, S., Tsujitani, S., Korenaga, D. and Sugimachi, K. (1992) Predictors of lymph node metastasis in early gastric cancer, Br. J. Surg., 79, 245-247. [30] Dvorchik, I., Subotin, M., Marsh, W., McMichael, J. and Fung, J.J. (1996) Performance of multi-layer feedforward neural networks to predict liver transplantation outcome, Method Inf. Med., 35, 12-18.
CancerLetters109(1996)141-148
CANCER LETTERS
Prediction of lymph node metastasis in gastric cancer patients with neural networks Karin Drostea,*, Elfriede Bollschweiler”, Thomas Waschulzikb, Thorsten Schiitzb, Rolf Engelbrechtb, Keiichi Maruyamac, J. Riidiger Siewerta “Department of Surgery, Technische Universitiit Miinchen, Klinikum rechts der Isar, Miinchen, Germany bGSF-Forschungszentrum fiir Umwelt und Gesundheit GmbH, MEDIS-lnstitut fiir Medizinische Informatik und Systemforschung, Neuherberg, Germany ‘Department of Surgical Oncology, National Cancer Centre Hospital, Tokyo, Japan
Received25 July 1996;accepted10 August1996
Abstract Artificial neural networks are a kind of pattern classifiers, with growing acceptancein medical and biological research.We applied a single layer perceptronto data of 4302 patients from National Cancer Centre in Tokyo and comparedthe results to the Maruyama diagnostic system (MDS) and classic statistical analysis with logistic regression. While logistic regression reachedno sensitivity and a specificity of 1.00 in median, MDS had a sensitivity of 0.74 and a specificity of 0.75 in median. The perceptron reacheda median sensitivity of 0.83 and a median specificity of 0.71. Keywords:
Artificial neural networks; Feedforward neural network; Gastric cancer; Lymph node metastasis;Computerized
diagnosis
1. Introduction Staging in gastric cancer is of special interest for therapeutic decisions. While T-category and organ metastasis can be determined with high reliability by medical imaging techniques like ultrasonography or computer tomography, there is still no method for accurate preoperative prediction of lymph node metastasis (LNM). Computer-aided diagnostic systems may offer new possibilities to this problem. * Corresponding author.ChirurgischeKlinik undPoliklinik, Klinikum rechtsder Isar der TU Miinchen,IsmaningerStmsse22, Manchen,Germany.TI:~.:+49 89 41404733.
Professor Maruyama developed a diagnostic system (MDS) based on data from National Cancer Centre in Tokyo (NCC) that allows preoperative prediction of LNM and prognosis for patients with gastric cancer [I]. This computer-aided diagnostic system has been validated among others with data from the Technische Universitst Miinchen [2]. In order to improve the results for correct prediction of lymph node involvement we developed a diagnostic support system using artificial neural networks (ANN) for preoperative prediction of LNM. The aim of this study was to compare a neural network system for preoperative assessment of lymph node metastasis in gastric cancer with the MDS.
0304-3835/96/$12.00 0 1996ElsevierScienceIrelandLtd. All rightsreserved PII 0304-3835(96)04438-l
142
K. Droste et al. I Cancer Letters 109 (1996) 141-148
2. Material and methods 2.1. Neural networks ANN are results of research in informatics, especially in artificial intelligence. Neuroinformatics, an interdisciplinary field of research within informatics, mathematics, physiology and anatomy, has two aims. On the one hand it analyses the brain functions by simulation. Suitable models are built with basic information processing techniques. For this purpose neuroinformatics uses the knowledge of biological brain research. Structures like neurons, axons, dendrites and synapses are modelled in data structures. Processes, e.g. excitation forming and conduction of nerve pulses, are simulated by mathematical functions. On the other hand, neuroinformatics apply ANN in practice. ANN are tolerant of missing values or inconsisten-
ties in data which often occur in medical data acquisition. They are supplied with self-learning mechanisms which simulate learning proceedings of human brain in a more or less biologically plausible manner [3]. ANN are known for being able to recognize and generalize correlations and rules from casebased databases, which then are applied to other ‘unknown’ data. They are also suitable to solve problems like constitution of hypotheses or pattern recognition. For these reasons they may be an appropriate method to analyze medical data. We use a simple variant of feedforward neural networks: the perceptron (Perceptron simulation utility, F. Grothe, GSF, Neuherberg, Germany). Rosenblatt first described this kind of ANN [4]. It consists of two layers of so-called neurons, an input layer and an output layer (Fig. 1). In this case the output layer includes two neurons, representing, respectively, metastasis and no metastasis of the lymph node
utput la Yer
SYnaP ses
input la Yer
Fig. 1. Diagram of perceptron. Output layer presents neurons as indicators for metastasis of LNG 1 input layer demonstrates neurons for the variables ‘Borrmann classification’ and ‘depth of invasion’ (Bl, Borrmann classification type 1; B2, Borrmam~ classification type 2; B3, Bonmann classification type 3; mm, mucosa; sm. submucosa; pm, muscularis propria).
K. Droste et al. I Cancer Letters IO9 (1996) 141-148
143
the weights of their synapsesthe activities of the output neurons are calculated (processing). This is illustrated in Fig. 2. It is similar to procedures of information processing in brain: multiple synapses lead information from many other nerve cells to one neuron. Incoming impulses are added up and transformed into neuron activity, which is representedby the frequency of leading impulses through the axon to other neurons. Synapsescan be of excitatory or inhibitory kind; this is indicated in the positive or negative weights of the connections of ANN.
Aw,(t) = p*Gj(t)‘A,(t)
w -weight, t - time, i - input, j -output, p - rate of learning, A - activity,
tag - target value, net - actual value
2.2. Data and statistics
*At)
A 2550 -Jz> L Ul
threshold
IleA - state of the nehvd Il-1owe.rlimit,ul-upperlimit
Fig. 2. Formula for learning and processing of ANN. Learning: w, weight; t, time: i, input; j, output; ~1,rate of learning; A, activity; targ, target value; act, actual value.
group. Each variable, e.g. depth of invasion or location of tumor, is organized in a group of neuronsin the input layer. A neuron within such a group is responsible for one value of the variable, e.g. infiltration of submucosawithin the neurons for the variable depth of invasion. Furthermore, each neuron of the input layer is connected to every neuron of the output layer. These connections are called synapses.In the phase of training the synapses are supplied with weights, dependenton the strength of the connection and the predictivity (learning). Even negative weights are possible. The mathematical algorithm underlying the training procedureis a kind of supervisedlearning 151. According to the activity of the input neurons and
A data setwith 4302 recordsfrom NCC of the years 1969- 1988was at our disposal.This data set includes information about patients’ age and gender, size, site and depth of invasion of the tumor, Borrmann classification [6] and variables for the number of affected lymph nodes in each of the 17 lymph node groups (LNG, Table 1). Age of patients ranges from 20 to 90 years with a meanof 58.2 years; distribution of sex is male: female 2: 1. The maximal diameter of tumor rangesfrom 1 to 220 mm with a mean of 53.4 mm. The variable Borrmann classification includes 2.4% type 1 (non-ulcerated polypoid), 17.5%type 2 (ulcerated elevated with well-defined border), 31.6% type 3 (ulcerated with partially diffuse infiltrating border), 8.0% type 4 (difTable 1 Lymph node location according to the Japanese Research Society for Gastric Cancer (JRSGC) LNG- 1 LNG-2 LNG-3 LNG-4 LNG-5 LNG-6 LNG-7 LNG-8 LNG-9 LNG-10 LNG-11 LNG-12 LNG-13 LNG-14 LNG-I5 LNG-16 LNG-17
Cardia right Cardia left Lesser curvature Greater curvature Suprapyloric Subpyloric Along left gastric artery Common hepatic artery Coeliac axis Splenic hilus Splenic artery Hepatoduodenal ligament Retropancreatic Mesenteric root Along middle colic vein Along abdominal aorta Other location
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fusely invading) tumors and also 40.5% early cancer (Table 2). The depth of invasion was distributed as follows: 22.3% mucosa, 18.2% submucosa, 9.6% muscularis propria, 7.3% subserosa, 6.6% serosa suspected, 22.1% serosa definite and 13.9% neighboring organs (Table 2). The location of tumor was coded in two variables: one variable for the longitudinal and one for the circular location. Both variables have four digits in hierarchical order. First place takes the location of the main tumor mass, second place takes location with next smaller tumor mass and so on. Only the main location was used in this part of the study. The distribution of values for the longitudinal location was 19.3% in the upper third, 42.9% in the middle third and 37.8% in the lower third. The circular location was lesser curvature in 44.9%, greater curvature in 12.3%, anterior wall in 14.5%, posterior wall in 20.2% and circular in 8.1% (Table 3). Results of MDS and ANN were proved by statistical analysis with logistic regression (‘SPSS for Windows’, Release 5.0.2, SPSS Inc. 1989-1993). This method is comparable to our ANN because it regresses a dichotomous dependent variable (output) on a set of independent variables (input). Independent variables may be metric or categorical. The parameters of the model are estimated using the maxiTable 2 Distribution of Bonmann classification and depth of invasion o/o Borrmann classification Early cancer TYPO ’ Type 2 Type 3 Type 4
40.5 2.4 17.5 31.6 8.0
Depth of invasion Mucosa (mm) Submucosa (sm) Muscularis propria (pm>1 Subserosa (SS) Serosa suspected (Sl) Serosa definite (S2) Neighboring organs
22.3 18.2 9.6 7.3 6.6 22.1 13.9
Borrmarm classification: type 1, non-ulcerated polypoid; type 2, ulcerated elevated with well-defined border; type 3, ulcerated with partially diffuse infiltrating border; type 4, diffusely invading.
Table 3 Distribution of tumor location
Location longitudinal Upper third Middle third Lower third
19.3 42.9 37.8
Location circular Lesser curvature Greater curvature Anterior wall Posterior wall Circular
44.9 12.3 14.5 20.2 8.1
mum-likelihood method. Results are predictions or estimates of probability of occurrence of an event (affection or non-affection of one LNG). 3. Results At least three different methods of prediction of lymph node metastasis have been evaluated: ANN, MDS and logistic regression. Table 4 shows the comparison of sensitivity and specificity of each of this methods. Results are presented for each of the three compartments of LNG and standardized on all 17 LNG (Table 1). The number of LNG is according to the General Rules for the Gastric Cancer Study in Surgery and Pathology [7]. Logistic regression reaches very high specificity (median 1.00, mean 0.98), whereas the sensitivity is very low, only 0.26 in mean with a median of 0.00. If used for preoperative staging this could lead to systematic overstaging of N-category. Especially for the LNG 7- 17, for which an accurate prediction is required, there is no more sensitivity given. Output of the MDS is based on group means of patients with same background. Patients are grouped by the variables Borrmann classification, depth of invasion and main location and a probability value for affection of the LNG is calculated. Sensitivity and specificity were evaluated at various thresholds between probability values of 0.1% and 20%. Given a threshold of 7%, above that the LNG is classified as affected; errors in both directions are nearly similar. The median specificity is 0.75 and the median sensitivity is 0.74. Fig. 3 shows the receiver operator char-
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K. Droste et al. I Cancer Letters 109 (1996) 141-148 Table 4 Sensitivity and specificity of different methods of prediction of lymph node metastasis in patients with gastric cancer
LOGREG MDS ANN 1 ANN2 ANN3 ANN4
Sensitivity LNG 1-6
Specificity LNG 7-11
LNG 12-17
Median
LNG l-6
LNG 7-11
LNG 12-17
Median
0.27 0.93 0.9 1 0.83 0.56 0.87
0.00 0.75 0.92 0.87 0.47 0.88
0.00 0.07 0.86 0.77 0.54 0.70
0.00 0.74 0.91 0.84 0.57 0.83
0.96 0.51 0.54 0.68 0.76 0.71
1.00 0.62 0.41 0.66 0.83 0.66
1.oo 0.96 0.48 0.66 0.63 0.72
1.oo 0.75 0.5 1 0.68 0.69 0.71
Logistic regression: Borrmann classification, depth of invasion, main location (saturated model). MDS: threshold 7%. ANN I input = Borrmann classification; ANN 2 input = depth of invasion; ANN 3 input = man location; ANN 4 input = Borrmann classification, depth of invasion, main location.
acteristic curve (ROC curve) of MDS with additional points for ANN. The ROC curve is used to describe the accuracy of a. test over a range of cut-off points. Area under the graph is a scale for the overall accuracy of a test [8]. Results of ANN are demonstrated for different steps of the study.. First, the predictive value of different variables on L,NM was tested using only one variable as input for ANN. At least four variables demonstrated predictive importance for LNM: depth
of invasion, maximal diameter, Borrmann classification and location of the tumor. Other variables like age, sex or histology have low or no bearing on prediction of LNM. Using a combination of the same variables as the MDS a median sensitivity of 0.83 and a median specificity of 0.71 could be achieved.
4. Discussion LNM is one of the most important prognostic fac-
Fig. 3. ROC-curve of MDS. Squares, MDS; circles, ANN.
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tors in patients with gastric cancer [9-121. Staging of lymph node metastasis is important not only for the choice of operation techniques or postoperative tumor staging but also for accurate preoperative staging and therapy decision including conservative therapies. The accurate prediction of actual LNM may avoid overstaging of patients with gastric cancer and lead to more certainty in therapy decision. Until now there is no method available for correct preoperative staging of N-category in gastric cancer patients. Even the accuracy of N-staging by computer tomography or endoscopic ultrasonography, if not limited to regional LNG, is unsatisfactory [ 13- 151. Computer-aided diagnostic systems and expert systems are used by a minority of clinicians, but if tested in controlled clinical trials they may offer new possibilities to medical education and clinical practice [16,17]. For some applications in medicine ANN were developed, e.g. diagnosis of acute pulmonary embolism [ 181, early detection and diagnosis of cancer [19] or evaluation of the MONICA-study of chronic heart disease [20]. A review of the huge flexibility of ANN and their diagnostic accuracy in a wide variety of areas is illustrated in [21]. We applied ANN to pretherapeutic prediction of LNM in patients with gastric cancer in comparison to the MDS and statistical analysis with logistic regression. Statistical analysis with logistic regression would lead to systematic over-staging of N-category and therefore possibly to more aggressive therapy than necessary. Dependency of postoperative morbidity rate on extend of lymph node dissection is still in dispute as well [23-261. The postoperative complication rate may be decreased by limiting the extend of lymph node dissection [22], but this is possibly purchased by an avoidable relapse and a shorter survival time of the patient. Thus, it is required to keep the probability of error for the prediction ‘not affected’ as small as possible. That is the way logistic regression works; the error for the prediction ‘not affected’ is minimized under purchasing a high error rate for the prediction ‘affected’. Similar results could also be achieved with MDS by using an appropriate threshold. Implementing a priority-function to ANN-tools will offer this ability to ANN as well. Results of this study already demonstrate that ANN are a suitable method for preoperative prediction of lymph node metastasis in gastric cancer. In general
the single layer perceptrons applied to that, suit very well for less complex problems or constitution of hypotheses. First, insertion of a-priori knowledge about physiological relations or special orders within variables into generation of ANN was deliberately abandoned. Each variable was explored about its predictive value on lymph node assessment, by taking it as only input for the ANN. Results of this series of experiments prove that it is possible to discover predictive variables out of a medical data base by using ANN. We discovered four variables as most important for prediction of LNM: depth of invasion, maximal diameter, Borrmann classification and location of the tumor. The same variables, except maximal diameter, are used for LNM-prediction in MDS. Different combinations of the four input variables in ANN prove the model used in MDS to be well-founded. Both methods lead to valid results. Correlation of LNM with T-category, site and size of the tumor or depth of invasion is confirmed in other studies [27-291; additionally, we found the Borrmann classification a very important predictive variable for LNM. We continue this study in order to increase the expectancy of hitting for both the prediction of affected and not affected lymph node groups, because higher sensitivity and specificity may avoid pretherapeutic overstaging or understaging and lead to more certainty in therapeutic decision. There are miscellaneous chances to reach this goal. First the input of variables may be improved. In the original data set the location of tumor was coded in two variables, one variable for the longitudinal and one for the circular location. Both variables have four digits in rank order. First digit takes the location of the main tumor mass, second digit takes location with next smaller tumor mass and so on. Advancement of coding of the input values enabled us to take into account our knowledge about the content of variables, e.g. the rank order of locations by tumor mass or the relation between longitudinal and circular location. Modelling the input layer and expansion of the network structure within the meaning of a ‘pseudo multi layer perceptron’ offers more possibilities for data processing with ANN and consideration of additional information about lymph node metastasis. At least the algorjthm of the ANN may be improved in special aspects [30]. Considering a priority function permits the weighting of errors of prediction. This part
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of the study is not yet concluded but to some extent clear improvements could be achieved. Furthermore, if a sufficient database with data about computer tomography, sonography and endoscopic ultrasonography is available, these data could be integrated in the ANN. Additional possibilities to improve the prediction of lymph node metastasis are the hierarchical combination of networks or the connection to a neural knowledge base:, containing information about the lymph system and lymph outlet of the stomach or ways of lymph node metastasis forming in gastric cancer. Several ANN managing different problems could be integrated into an expert system and periodical training phases with actual patient data would guarantee actuality of the weights of the synapses by consideration of changes, e.g. of epidemiology. References [l] KampschiKr, G.H.M., Maruyama, K., van de Velde, C.J.H., Sasako, M., Kinoshita, T. and Okabayashi, K. (1989) Computer analysis in making preoperative decisions: a rational approach to lymph node dissection in gastric cancer patients, Br. J. Surg., 76, 905-908. [2] Bollschweiler, E., Bottcher, K., Holscher, A.H., Sasako, M., Kinoshita, T., Maruyama, K. and Siewert, J. (1992) Preoperative assessmem of lymph node metastasis in patients with gastric cancer: evaluation of the Maruyama computer program, Br. J. Surg., 79, 156-160. [3] von Neumann, J. (1958) The Computer and the Brain. Yale University Press, New Haven, pp. 66-82. [4] Rosenblatt, F. t 1958) The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., 65, 386-408. [5] Widrow, B. and Hoff, M.E. (1960) Adaptive switching circuits. IRE WESCON Convention Record, pp. 96-104. IRE, New York. [6] Borrmann, R. (1926) Geschwtilste des Magens. In: Handbuch der speziellen pathologischen Anatomie, pp. 864-871. Editors: F. Henke and 0. Lubarsch. Springer, Berlin. [7] Japanese Research Society for Gastric Cancer (1981) The general rules for the gastric cancer study in surgery and pathology. Jpn. J. Surg., 11, 127-145. [8] Fletcher, R.H., Fletcher, S.W. and Wagner, E.H. (1988) Diagnosis, In: Clinical Epidemiology, pp. 42-75. Editors: N. Collins, C. Eckhart and G.N. Chalew. Williams and Wilkins, Baltimore, MD. [9] Secco, G.B., Fardelli, R., Campora, E., Rovida, S., Beatini, M., Larghero, G., Testa, T. and Prior, C. (1992) Extension of lymph node dissection and survival in primary gastric cancer, Int. Surg, 77, 242-247. [IO] Bottcher, K., Becker, K., Busch, R., Rider, J.D. and Siewert, J.R (1992) Prognosefaktoren beim Magencorcinom, Ergeb-
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