Neural networks analysis of astrocytic gliomas from MRI appearances

i i

CANCER LETTERS

1 Cancer Letters 118 (1997) 69-78

Neural networks analysis of astrocytic gliomas from MRI appearances’ Parviz Abdolmaleki”,

Futoshi Mihara, Kouji Masuda, Lawrence Danso Buadu

Department of Radiology, Faculty of Medicine, Kyushu University, Maidashi 3-l-1, Higashi-ku, Fukuoku 812-X?. iapm Received 17 February 1997; received in revised form 7 April 1997; accepted 7 April 1997

--. Abstract

._-..____----~

A three-layeredbackpropagationneural network was developed to differentiate malignant from benign brain tumors in a group of patients with astrocytic gliomas. The MRI findings of 43 patients were reviewed before biopsy by three neuroradiologists independently. This provided a databasemadeup of 129 patients’ recordseach of which comprised I3 parameters derived from pre- and post-contrastMR images.The network’s generalizing ability was then testedto predict the outcome of biopsy in 36 new cases and its performance compared to that of radiologist using ROC analysis. The output of the network with and without radiologists’ impression yielded a better diagnostic performancewith relative ROC areasof 0.94 and 0.9 1.

respectively, compared to 0.84 obtained by radiologist. These results demonstratethat the neural network can effectively differentiate malignant from benign brain tumors. 0 1997 Elsevier Science Ireland Ltd. Keywords:

Astrocytoma; Magnetic resonance imaging (MRI); Artificial

neural networks (ANN); Receiver operating char-

acteristic curve (ROC)

1. Introduction Accurate radiological diagnosis is very important for the clinical management and prognostic assessment of patients with astrocytic gliomas. Previous correlative imaging studies using computed tomography (CT) and magnetic resonance imaging (MRI) have attempted to determine which features best reflect tumor grade and predict prognosis [l-3,29]. Based on the knowledge of the relationship of such features to tumor grade, neuroradiologists predict IRS n m author. Tel.: ;-g)R,p d’g e

+81 92 6425695; fax: +81 92

’ This work was presented as a scientific paper and electronic exhibition at the 1996 annual meeting of the American Roentgen Ray Society. San Diego, CA, USA.

tumor grade usually on a three-tiered scheme 111. Despite the excellent sensitivity of MR imaging for the detection of astrocytic gliomas, there is still some difficulty in the prediction of malignancy in these patients [ 1,5]. Biopsy is therefore often done to confirm the diagnosis, despite the disadvantages of higher cost, and the stress it imposes on patients. Consequently a computerized second opinion would be helpful to aid radiologists in differentiating malignant from benign brain tumors, especially in borderline cases. Here, we report our investigation of an approach in which an artificial neural network (ANN) was designed to work as an automated classifier. ANN is a computer algorithm whose structure and function are based on a model of the structure and learning behavior of biological neurons. This algorithm is

0 1997 Elsevier Science Ireland Ltd. All rights reserved 0304-3835/97/$17.00 PI1 SO304-3835(97)00233-4

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P. Abdolmaleki et al. / Cancer Letters 118 (1997) 69-78

typically employed to classify a set of patterns into one of several classes. The classification rules are not written into the algorithm, but are learned by the network from examples. The basic elements of ANN are processing elements (Pes) and weighted connections. The collection of processing elements defined as layers includes the input, one or more hidden, and an output layer. Each processing element receives values from all its input connections, performs a previously defined mathematical operation and produces a single output value. The connection weights store the information in the form of weight matrices [6]. The value of the connection weights is determined by the neural network learning procedure. Learning therefore is the most appealing quality of ANN which could be either ‘supervised’, where sample input-output pairs are presented, or ‘unsupervised’, where the network organizes itself. The most successful algorithm in solving clinical diagnostic problems so far has been the backpropagation learning method. In this method, the partial derivative of an error criterion with respect to the weights are adjusted as the negative gradient to minimize the error function [6-91. In the clinical setting, ANN has drawn a lot of attention during the past decade. It has been found to be useful in pattern recognition, thus highlighting its potential diagnostic utility in radiology [ 10-161. The objectives of this study were: (1) to apply neural network as a decision making aid to support radiological diagnosis based on data extracted from pre- and post-contrast MR images; (2) to test the ability of the system to recognize new examples as an expert system; (3) to compare the diagnostic performance of neural network with radiologist in the prediction of the malignancy or benignity of astrocytic gliomas.

2. Materials

and methods

In this study we used three-layer, feedforward neural network with a backpropagation algorithm, using momentum for training. The system was designed to predict the probability of malignancy or benignity on the basis of features that had been extracted from pre- and post-contrast MR images. For the training of the neural network, a series of 43

adult patients with histologically proven supratentorial astrocytic gliomas were initially selected. Thirtythree of these were malignant (nine anaplastic astrocytomas and 24 glioblastoma multiformes) and ten benign (low-grade astrocytomas). The patients included 25 males and 18 females whose ages ranged from 26 to 78 years (mean 38 years). MR imaging was done with a Magnetom 1.5-T unit (Siemens, Iselin, New Jersey) in 39 patients, a Signa 1.5-T unit (GE Medical Systems, Milwaukee) in two patients, a Gyroscan 1.5-T unit (Philips, Shelton, Connecticut) in one patient, and a Vista 1.0-T unit (Picker, Highland Height, Ohio) in one patient. Pre- and post-contrast Tl-weighted (TlW) imaging were performed at 500-760/17-30/2 (repetition time, ms/echo time, ms/ number of excitations) and pre-contrast T2 weighted (T2W) imaging at 2300-3000/90-100/l (Fig. 1). Other MR parameters used were a 256 x 128, 192, or 256 matrix, a 20-24 cm field of view, and a 4-7 mm slice thickness. Gadopentate dimeglumine (Magnevist, Berlex, Cedar Knolls) at 0.1 mmol/kg body weight was administered intravenously for all postcontrast studies. Three neuroradiologists (FM, YN, MR) without knowledge of pathological findings reviewed the images independently and graded their findings on 13 features (multiplicity, signal intensity on TlW imaging, signal intensity on T2W imaging, edema, heterogeneity, hemorrhage, border definition, mass effect, contrast enhancement, ring enhancement, tumor extent, location, cyst formation). All features were scored as 0, 1 or 2 based on their extent or severity. When tumor enhancement was as bright as fatty tissue, it was considered marked. Any size ring enhancement was considered positive. Tumors exhibiting enhancement of the entire tumor area were scored by a combination of positive contrast enhancement (1 or 2 in weight) but no ring enhancement (0 in weight). Table 1 shows the distribution of grading of MR parameters of 43 patients by three neuroradiologists as well as the agreement rate between them. All the raw scores of the MR parameters of each observer (a total of 129 readings) were fed into a three-layer feedforward neural network to map the MR imaging findings to the corresponding pathological results in a supervised manner. For the simulation of the neural network, all the quantitative data were normalized between 0 and 1 according to the maximum value of each feature in the data set. The normalized data were

P. Abdolmaleki et al. / Cancer Letters I I8 ( 1997) 69- 78

Fig. 1. MR images of a representative malignant case (Glioblastoma In nultiforme). (A) Axial TZ-Weighted image (TRITE : 25@Y90 ms) demonstrates a solitary mass with heterogeneous high signal intensity in the right frontal lobe. Marked perifocal edema and mass effect are seen. (B) Axial post-contrast Tl-weighted image (TWE = 500/17 1TlS ) demonstrates marked enhancemen and ring enhancement of the rumor.

Table I Distribution of grading of MR parameters of 43 patients by three neuroradiologists and their agreement rate in evaluation of each parametel Parameter

Multiplicity SI on TlW imaging SI on T2W imaging Edema Heterogeneity Hemonhage Border definition Mass effect Contrast enhancement Ring enhancement Tumor extent Cyst formation

Observer 1

Observer 2

Observer 3

MR grade

MR grade

MR grade

0

1

2

0

1

2

0

1

36 3 0 12 7 33 10 16 16 18 33 14

3 19 8 17 23 5 26 14 13 2 3 6

4 21 35 14 13 5 I 13 14 23 7 23

36 4 1 9 14 27 21 18 17 17 33 16

2 8 10 24 16 8 19 19 10 5 5 I

5 31 32 IO 13 8 3 6 16 21 5 26

32 2 0 I0 II 31 28 23 19 17 36 IO

6 13 13 26 4 4 8 14 8 4 3 4

The rate agreement between three neuroradiologists in evaluating location of the tumor was 100%

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Table 2 Distribution of evaluated parametersof MR images of 79 patients which presentedto the neural network in the training and testing procedure Parameter

Multiplicity No (‘8 Equivocal (1) Yes (2) Sls on TlW imaging Is0 (0) slightly low (1) Imv (2) SI on T2W imaging Is0 (0) Slightly high (1) High (2) Edema Mild (0) Moderate (1) Marked (2) Heterogeneity Mild (0) Modcrate (1) Marked (2) Hemorrhage No (0) Equivocal (1) Yes (2) Border definition Well circumscribed (0) Poorly circumscribed (1) Infiltrative (2) Mass effect Mild (0) Moderate (1) Marked (2) Contrast enhancement Mild (0) Moderate (1) Marked (2) Ring enhancement No (0) Equivocal (1) Yes (2) CMCC No (0) Equivocal (1) Yes (2) Cystlnecrosis No (0) Equivocal (1) Yes (2)

Training set (n = 43) (three readers)

Testing set (n = 36) (one reader)

Benign (n = 30)

Malignant (n = 99)

Benign (n = 7)

Malignant (n = 29)

29 1 0

74 11 14

5 1

17

3 8 19

6 32 61

0

0 5

2.5

1 26 72

20 7 3

12 56 31

3 3

7 12 10

19 9 2

13 34 52

5 2 0

3 10 14

27 3 0

64

14 21

5 2 0

26 1 2

11 11 8

45 45 9

4 2 1

15 12 2

22 5 3

35 42 22

4 3 0

11 14 4

29 1 0

23 30 46

6 1 0

1 12 16

28 2 0

24 9 66

5 0 2

7 1 21

27 0 0

72 11 16

7 0 0

18 3 8

14 9 76

11

3 4 0

2 5

11 17 1 1 11 17

P. Abdolmaleki et al. / Cancer Letters I18 (1997) 69-78 Table 2 (continued)

Parameter

Location Frontal lobe (,I ) ParietaJ lobe (2) Temporal lobe (3) Occipital lobe (4) Basal ganglia and thalamus (5) intraventricular (6)

Training set (n = 43) (three readers)

Testing set (n = 36) (one reader:

Benign (n = 30)

Malignant (n = 99)

Benign (n = 7)

Mahgnant (n = 29,

15 9 0 3 0 3

29 30 34 0 6 0

4 I 0 2 0 0

‘1 2 5 0 1 0 --_---l_

“Number in parentheses represents raw score. %I, signal intensity compared to the white matter of the brain. ‘CMCC. crossing the midline through corpus callosum.

then fedforward to train the network. One reader (FM) was then asked to read the MR images of a series of 36 different cases(23 men, 13 women; aged 27-76 years; mean age 54 years), including 28 malignant and seven benign, and asked to rank the extracted parameterswith the samecriteria as were used for the training set. This new database was then usedto test the generalizing capability of the trained network in terms of accuracy, sensitivity and specificity using the receiver characteristic curves (ROC) analysis. Table 2 summarizes the radiologic parameters used as inputs into the network during the training and the testing procedure. Finally, in order to assessthe influence of the radiologist’s impression on the performanceof the neural network, we presented the above-mentioned parameters plus the radiologist’s impression to the neural network. To achieve this, the simulations with and without the final observer impressions were carried out. In summary, the following simulations were performed and compared: (1) using data extracted from pre- and post-contrast MR images by three neuroradiologists the network was trained on 13 featuresin 43 adult patients; (2) the trained network was tested on 13 features extracted by one radiologist on 36 new cases;(3) using data extracted in (1) plus the radiologist’s impression the network was again trained on 14 features (13 extracted parametersplus radiologist’s impression); (4) the ANN was again testedusing radiological findings in (2) plus the radiologist’s impression (in total 14 features). The results of these simulations were then compared to results obtained

by the radiologist. 2.1. Neural networks structure The neural network which was employed in this study had three layers. The first layer consisted of 14 input elements,each of which correspomiedto an MR imaging finding; eight process elements in the hidden layer and one process element in the output layer, in which 1 was usedto representmalignant and 0 for benign (Fig. 2). In order to determine the best optimized structure for the neural network, we simulated a I number of neural networks by varying the number of hidden nodes, iterations and learning rates. Using random initial weights in the range of -0.5 to 0.5 the simulations were done by changing one parameter while keeping the other parametersconstant.In all the simulations the sum square error (SSE) was used as an index of the learning efficiency of the network during the training process.The sigmoid function was then used as an activation function for each of the units in the network and a backpropagation algorithm was used for training. The optimum learnia@rate, which adjusts the weight matrixes for both layers, was 0.3. The momentum coefficient, which enhancesthe stability of the network and decreasesthe training time, was 0.9. Briefly, basedon the learning process,the network tries to modify fixed weight matrixes and &just them so that for every training input it can pro&tce the desired output [7-91. Accordingly. the network was

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Multiplicity SI on TlWl SIon TZWI Edema Heterogeneity Hemorrhage BorderDefinition MassEffect ContrastEnhancement CMTCC RingEnhancement CystFormation Location Radiologist’sImpression BiasNeuron Input Layer

Letters 118 (1997) 69-78

Malignant Benign

Hidden Layer

Output Layer

Fig. 2. Schematic diagram demonstrating the topology of a three-layer, feedforward neural network with 14 input nodes, eight hidden nodes and one output node which presented 0 for benign and 1 for malignant tumors. Each node is connected to ah nodes in the next layer through the links with a weight matrix element. These weight matrix elements are modified during the learning process, so that they would be able to produce a desired output for an input vector. Bias neurons which only accept constant input (1) are placed in input and hidden layers in order to provide a non-zero output in the case of all zero inputs.

presentedwith pairs of input-output vector patternsin a supervised manner. Each input vector was fedforward through the layers to obtain an output. The resultant error, the difference betweenthe desired and calculated output, was then propagated backward to minimize the calculated error to the level of the error goal. In this study, error goal was defined to be equal to 0.02. The training patterns were presented several times, until the desired output was achieved. The training algorithm thus minimized the mean SSE between the desired and actual network output following an iterative gradient search [691.

Finally, after the network had beentrained perfectly in each simulation the testing caseswere presentedto the trained network giving it a diagnostic output value in the range of 0- 1. Our network was trained perfectly with 562 learning processesand 100 iterations in each learning processwithin 2 h on a Gateway 2000 personal computer (Pentium 90 MHz, IBM compatible machine). The software used to construct the neural network was written locally in MATLAB program-

ming language. This is a powerful language with a rich library in matrix manipulation. 2.2. Performance evaluation

We usedROC analysis to evaluatethe performance of neural network approach and that of radiologists. We applied the ROCPIT software for Apple Macintosh based on the Charles E. Metz algorithm [17]. In RGC analysis, diagnostic performance is reported in terms of two indices: the true-positive fraction and the false-positive fraction. The true-positive fraction is delined as the fraction of actually malignant casesthat are correctly diagnosed as malignant (i.e. sensitivity). The false-positive fraction is defined as the fraction of actually benign casesthat are incorrectly diagnosed as malignant (i.e. l-specificity) [ 181.In this regard, after the network had been trained perfectly in each simulation the testing set was presentedto the trained network giving a diagnostic output vector in the range of O-l. The outputs of the testing set were then analyzed to determine

P. Abdolmaleki

et al. / Cancer

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1

0.8

OR

0.6

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E

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0.4

04

0.2

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0.4

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0.6

0.8

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0.6

0.8

FPF

FPF

Fig. 3. (a) Resulting receiver operating characteristic curve comparing the diagnostic performance of the neural network with that of the radiologist. The ‘nn’ and ‘md’ are referring to the neural network and the participating radiologist on the testing set. (b) Resulting receiveIoperating characteristic curve comparing the diagnostic performance of the neural network with and without radiologist’s impression. ‘The ‘nn‘ and ‘nn + md’ are referring to the neural network and the neural network with radiologist’s impression on the testing set

the true-positive and the false-positive fractions, which were then used for plotting the ROC curves. The area under the ROC curve (A,) was then used to compare the performance of ANN as well as the radiologist participating in the testing procedure

1171. To evaluate the performance of experienced observers, the three readers were asked to record their findings into one of five categories: 1 = benign, 2 = probably benign, 3 = possibly malignant, 4 = probably malignant, 5 = malignant. Similarly, to evaluate the performance of the neural network, the network output was classified into five categories: output in range of O-O.2 = benign, 0.2-0.4 = probably benign, 0.4-0.6 = possibly malignant, 0.6-0.8 = probably malignant, and output in range of 0.8- 1 :. EO malignant. In addition to evaluate the effect of observer impression on the performance of the network, simulations with and without the final observer impressions were carried out.

3. Results Sixty-two out of 79 patients (whose MR imaging

findings were used in the training. n = 43, and testing process, n = 36) (78%) were malignant and 17 (22%) benign. The majority of malignant cases (53. 67%) were glioblastoma multiforme. Among the evaluated features the presence of ring enhancement is considered the strongest indication of malignancy with the highest correlation (Y = 0.60, P = 0.002) with pathological results followed by degree of contrast enhancement (r = 0.58, P = 0.002), tumor heterogeneity (r = 0.50, P = 0.001) and the extent of edema

(1.= 0.42, P = 0.002). To evaluate the capability of neural network to learn the particular benign and malignant patterns presented in the training set, we initially used the same database to train the neural network and to test the neural network after it had been trained. The neural network’s output yielded a perfect ROC curve (A, == 1) which shows that the neura1 network learned perfectly all of the presented patterns and was capable of recognizing all of them correctly. The testing set (36 new patients’ records) was then used to find out w-hether the ANN is able to differentiate between new patterns. In this regard, the performances of the neural network and the radiologists were plotted as ROC curves (Fig. 3a). The area under the ROC curves

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P. Abdolmaleki et al. /Cancer

Letters 118 (1997) 69-78

Table 3 Physician and ANN predictions in 36 test cases Evaluator

Benign TN

Malignant TP

Correlation with pathology (r)

Radiologist Neural network without radiologist’s impression Neural network with radiologist’s impression

417 5n

25129 27129 28129

0.5641 0.8719 0.8919

517

(A,) for the neural network on the testing set was A, = 0.9118 rt 0.0427 (area index f standard deviation) with an accuracy of 89%, and for the radiologist was A, = 0.8491 f 0.0709 with a maximum of 80% accuracy. The relative area under the ROC curves (A,) for the neural network with radiologist’s impression on the testing set was A, = 0.9408 + 0.0271 with an accuracy of 92% (Fig. 3b). Table 3 shows the predictions of the radiologist for the testing set (n = 36) compared with the predictions of neural network with and without the final observer’s impression and corresponding correlations with pathological findings. The output of neural network showed a significant difference in diagnostic performance compared with that of the radiologist alone (two-tailed P = 0.003). On the other hand, applying the final observer’s impression as a extra input to the network yielded an increased accuracy from 89% for the neural network without radiologist’s impression to 92% of the neural network with radiologist’s impression. There was also a significant difference in diagnostic performance of ANN using radiologist’s impression compared with the radiologist alone (two-tailed P = 0.012). However, the difference between the performance of the neural network with and without the final observer’s impression was not statistically significant (P = 0.072).

4. Discussion Evaluating the grade of malignancy of astrocytic gliomas is essential because it directly affects patients’ clinical management and is of prognostic importance. The accuracy of grading is strongly dependent on the association of certain features such as edema, hemorrhage, cyst formation and the degree of contrast and ring enhancement with benignity or malignancy [19,20]. This study reports the potential usefulness of the application of an ANN as a support-

ing system for diagnosis based on data extracted from pre- and post-contrast MR images. Our results show that a three-layer feedforward neural network with a backpropagation algorithm can be trained to successfully perform this diagnostic task. Although the interpretation of the results of a multilayered network is difficult, the accuracy of the network suggests its potential usefulness in making simultaneous associations among a large number of parameters. Recently, Christy et al. [21] developed a neural network for grading astrocytic gliomas with a diagnostic accuracy of 61% which was comparable to that of radiologists. They found a superiority for neural network compared with the traditional multiregression method in the grading of astrocytic gliomas. Kolles and coworkers developed a neural network approach based on four morphometric parameters obtained from stereotactically biopsied astrocytomas. They also reported higher performance for the neural network compared to the classical multivariate discriminant classification analysis for automated grading of astrocytomas [22]. Kischell and his group also applied four types of neural networks (two supervised and two unsupervised) for the classification of brain compartments and head injury lesions using MR imaging. They found that the backpropagation neural network combined with feature conditioning produced results closely in agreement with that of experienced observers [23]. Apart from the differences in the structure of the network or database, there was also a difference in the final output of the network. In selecting the different parameters for evaluation, we placed emphasis on applying neural network to predict the degree of malignancy. To achieve this purpose we included ring and contrast enhancement, edema, and heterogeneity because the presence of ring enhancement, a strong degree of contrast enhancement, extensive edema and marked heterogeneity have been reported to be the best general predictors of a malignancy [l-5,24-27]. Mass effect and cyst

P. Abdolmaleki et al. /Cancer Letter.7 118 (10971 6Y-78

formation were included because of a report by Dean et al. [l] in which these two parameters were found to be the most important predictors of malignancy. Mild mass effect, edema, heterogeneity and the absence of hemorrhage which have been reported frequently as characteristic features of low-grade astrocytoma, as well as the signal intensity of tumor on T2-weighted imaging and tumor extent, were also assessed [ 1,19,24,28]. Among these features, the presence of ring enhancement is considered the strongest indicator of malignancy [ 191. The location was also evaluated because some reports indicate a propensity of glioblastoma multiforme for certain areas of the brain (more often in the frontal lobes than temporal lobes or basal ganglia) [ 1,281. Finally, the radiologist’s impression was added as an extra input in order to assessthe influence of radiological interpretation of the findings on the performance of the network. The justification for the use of the radiologist’s impression as an input lies in the fact that in the clinical setting the network is intended to support radiological diagnosis. Consequently, the evaluation of the diagnostic performance of the neural network with and without the radiologist’s impression was examined. The results of these two simulations were compared with that obtained by radiologists. The significant difference in area under the ROC curves (A,) for neural networks with and without the radiologist’s impression compared with that obtained by radiologists clearly indicates that applying artificial neural network as a decision making aid is supportive for the radiologist. In addition, the correct prediction of 33 of 36 of cases in the testing database demonstrates the ability of neural networks to recognize new patterns, and its potential usefulness in classification of benign and malignant patterns. A noteworthy point is that although a difference in the area under the ROC curves (A,) for neural networks with and without radiologist’s impression was apparent, it was not significant. This may be related to the different levels of experience of the reviewers during the training procedure. This was more common in the differentiation of low-grade astrocytoma from anaplastic astrocytoma. The best correlation between radiologist’s impression and pathological results was found for one of the reviewers (FM, r = 0.80) who was asked to read 36 new cases during the testing procedure, Using the radiologist’s impression as an additional

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input improved the accuracy of neural network from 88% to 92% which indicates the abihty of neural network to make complex associations among a lot of non-linear and dependent parameters by addressing them as proportional weights. Using this ability in making combinations between MR parameters with different weights, tumor classification can be more objective, automated and probably more consistent. Imaging is generally a matter of identifying and scaling features, then merging the features with various weights, into an overall diagnostic decision 130J. The final process of integrating each parameter into a final decision demands training and experience, and results in considerable interobserver variability 1301. The estimation of the probability of malignancy or benignity with the neural network assistance could increase the accuracy and consistency, especially among readers with little experience or in borderline cases. In addition, the ANN may also be helpful for resident training whereby optimal parameter weights can be ascertained. This could be more useful if the network has been trained using a large multi-institutional database. In any such attempted application, standardized weighting of parameters will be necesS3I-y.

In conclusion, we have established a neural network which is able to predict the degree of malignancy of astrocytoma using features extracted from pre- and post-contrast MR imaging. This network can be optimized to function better using a combination of radiological findings from MR imaging, histomorphological parameters and clinical data in a large population of patients.

Acknowledgements The authors are indebted to Dr. Yuji Numaguchi, Dr. Michael Rothman and Dr. Setsu Sate for their valuable assistance. References [I] B.L. Dean. B.P. Drayer, CR. Bird, R.A. Flom. J.A. Hodak. SW. Coons et al., Gliomas: classification with MR imaging, Radiology 174 (1990) 41 l-415. [2] P.J. Kelly, C. Daumas-Duport, B.W. Scheithauer. B.A. Kall, D.B. Kispert, Stereotactic correlation of computed tornogra-

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