ANN predicts locoregional control using molecular marker profiles of. Head and Neck squamous cell carcinoma

Transcription

1 ANN predicts locoregional control using molecular marker profiles of Head and Neck squamous cell carcinoma Final Project: 539 Dinesh Kumar Tewatia

2 Introduction Radiotherapy alone or combined with chemotherapy, plays a critical role in the management of head and neck squamous cell carcinoma. Identification of factors that help to predict the response of head and neck squamous cell carcinoma (HNSCC) to radiotherapy and in particular, assist identification of patients at risk of relapse may be useful in refining treatment strategies and improving overall clinical outcome [1]. The intention of this project was to explore how an artificial neural network (ANN) could be used for such predictions. Such The role of ANNs in this problem was considered because of their ability to learn relationship between a set of inputs and corresponding outputs without the need for a priori information regarding the relationship between the two. ANNs learn using a mathematical model based on a high-level abstraction of the learning process in biological neural systems. There are number of ANN methods; however, the techniques in this project concentrate on the multilayer perceptron (MLP), originally described by McCulloch and Pitts [2] and achieving widespread popularity and application after a suitable learning process was described by Rumelhart et al [3]. The architecture of an MLP consists of many identical units known as nodes which are analogous to neurons, the processing units in the brain. These nodes are structured in to number of layers connected together by weights, which are representative of inter-neuron synapses in the brain. Commonly there are three layers, the input, hidden and output layers. The input layer simply feeds information into the network whilst nodes in the hidden and output layers process information. During training the corresponding known outputs of the system are held in the output nodes to compare with the results produced by the network. The nodes in the hidden layer have no prescribed initial values and help to allow complex relationships between the input and output nodes to evolve. Information is propagated from the input nodes to the output nodes by calculating the sum on each node which is found by multiplying all the nodes in the previous layer (working left to right) by the weight connecting the two nodes in question and summing together. MLPs incorporate a non-linear activation function allowing them to learn non-linear relationships. This flexibility is useful when trying to learn complex relationships such as those based on clinical experience and human nature. In this project activation function for output layer is sigmoidal, linear over small range of values close to zero but saturates for large values and for hidden layers it is hyperbolic tangent function. The number of hidden layers is two with ten neurons. Once the values on the output nodes have been calculated, they are compared with the expected values and the difference used to change the interconnecting weights using backpropagation of errors. The implementation of the weight optimization was achieved by propagating a fraction of the calculated error back through the network. The magnitude of the change is determined by the learning rate α which has a value less than unity. As with every optimization algorithm there are limitations to the practical use. The algorithm can be improved by the addition of a momentum term η which augments consecutive changes in the same direction by including a fraction of the change from the previous iteration. This iterative process is repeated until the difference between the actual and expected values calculated for a group of validation cases, not

3 used in training, is minimized. The weights are then fixed and the ANN can be used to provide outputs for new cases. Predictive performance is evaluated by the leave-one-out or leave-someout cross-validation procedure. Materials & Methods The panel of markers was chosen to represent biological factors known to affect tumor response to fractionated radiotherapy, such as vascular density and expression of proteins involved in the regulation of cell cycle progression, proliferation, or apoptosis. Following are the markers which were used to generate the data used for this project 1. Ki 67: an antigen that is expressed by proliferating cells in all phases of the active cell cycle. 2. p53: a transcription factor that regulates cell cycle progression and apoptosis. 3. Bcl 2: an antiapoptotic molecule. 4. Cyclin D1: required for cell cycle progression at the G 1 -S transition. 5. CD31: for accessing the vascular density. From about 100 different feature vectors, the above mentioned molecular markers and the following parameters were used to construct the ANN model using MLP. Sex: 0 or 1 corresponding to Male or Female Age: Continuously varying variable (in years) Histological grade: Accept values from 0 to 4 corresponding to either well differentiated, moderately differentiated, poorly differentiated or Squamous cell (not specified) type of tumor. Tstage: Accept values from 0 to 4 corresponding to T1, T2, T3, T4 based on the extent of primary cancer including tumor size, at the time of diagnosis. Nstage: Accepts values 0 and 1 indicating nodal involvement. Currently, age, Tstage, Nstage and histology are clinical indicators that are used to predict (not quantitatively) the treatment outcome. Above all this, more importantly, the treatment that was used for each case/patient is also used as an input for developing the model. CHART or CRT is randomly picked to treat each patient. Output / Clinical End points All patients were observed for a period of 6 weeks, and then additional follow-ups at 8 weeks, 3 months and every 6 months till 5 years were performed. Local or nodal failure were recorded as a failure (value: 1) for locoregional control. Distant failure (value: 1) was defined as the appearance of distant metastasis outside of the irradiated volume. The time to failure for each

4 case was recorded for both locoregional and distant failures. Survival was not used as an output merely because in most of these cases, death can occur due to many other reasons other than cancer itself. Example of data that were fed as inputs to the ANN model is shown below. INPUT OUTPUT ki67 pattern p53 p53int Cd31 bcl2 cyclind sex hist age tstage nstage trt distmet dead locoreg Data Processing Raw data were obtained for a group of 402 patients who participated in the Medical Research Council trial of CHART versus CRT. This data set had over 100 different feature vectors out of which the feature vectors described above were extracted. Data were modified such that the only clinical outcome for a period of 365 days was considered. For example, if locoregional failure was not seen for a patient within 365 days, the value for locoregional failure was considered to be 0 (did not recur). This modification was done for distant failure as well. Many records in this data set had NULL values corresponding to some parameters mostly because the laboratory did not have sufficient samples or technology to tabulate the values. This brought down the size of the data set to 257 values. Out of 257 cases, 160 cases were used as training data and 97 cases were later used as labeled test cases to evaluate the model developed.

5 Model and Results In this project, a two layer MLP was used which used 13 input neurons, 10 neurons in the hidden layer and 2 output neurons. A learning rate (α) equal to 0.01 and momentum (η) of 0.8 was used. Number of epochs was set to 1000 with an epoch size of 64. These above mentioned parameters were found to be the best after several permutations and combinations. Hidden layers used tanh activation function whereas the output layer used sigmoidal activation function. For the training data set (160 cases in number), 8-way cross validation method was used to determine the average tuning error and the weight matrix corresponding to the highest classification rate was chosen to form the final model. Labeled testing data (Number of cases =97, not at all used in the training set) were then used to test the model parameters shown. For the 8 way cross validation technique, the highest classification rate obtained for the tuning set was equal to 100% and the average classification rate was 97.5%. The weight matrix chosen corresponding to the best classification rate for the tuning set is given below Hidden Layer Weights Output Weights For the testing data set, a classification rate of 100% was obtained for the above model

6 parameters. I also tried to develop the model for prediction of 2 year local and distant failure. An average tuning classification rate of % was obtained. For the testing data a classification rate of % was obtained. Discussion and Conclusion The results presented in this project were encouraging as exemplified and compared with the outcome predicted by the authors Buffa et al on which this project was based. The paper uses hierarchical clustering methods, which were used to form clusters based on molecular profiles only. Eight clusters were identified and each cluster varied between 24 and 68 patients. Bootstrapping on the cluster formation with 1000 samples showed that clusters 1 3 were very robust, with no misclassifications after bootstrapping. The average misclassification yield in cluster numbers 4 8 ranged between 21% and 29% of the cases. Weights of the input neurons to the hidden layer were observed closely and apart from the histology and t-staging which play a major role in determining the clinical outcome, the molecular profiles and treatment also play a major role in deciding the outcome. From among the molecular profiles, p53 intensity and Bcl-2 are the most important. This is in accordance with the observations published by Buffa et al. The ANN model designed in this project allows us to identify a subgroup of patients who might benefit from being randomized to the strongly accelerated continuous hyperfractionated accelerated radiotherapy (CHART) treatment schedule rather than conventionally fractionated radiotherapy (CRT). Consequently it helps for the specialized/customized treatment for every patient known as Theragnostics. ANN model is superior to multivariate logistic regression analysis in predicting the locoregional control and distant metastasis in terms of data handling. Survival outcome was somewhat misleading because if the patient dies after 365 days then it is not sure that patient died because of cancer or not. When using larger databases, ANN technology becomes increasingly useful for predicting outcomes. However, although ANN modeling is a powerful tool to simulate non-linear systems, linear modeling sometimes shows better predictive performance for linear systems. It is necessary to take in to account the structure of the input-output relationship before using ANN modeling to predict patient outcomes in clinical settings. For an ANN-based method to be considered as an alternative to the current methods a large number of patients would be needed to further improve training along with a completely independent set for validating the results. In the future, it will be necessary to test the prediction of MLP based model with only molecular profiles as the input neurons since that is where the future of radiation oncology lies.

7 References 1. Francesa M. Buffa, Soren M. Bentzen et al. Molecular marker profiles predict locoregional control of head and neck squamous cell carcinoma in a randomized trial of continuous hyperfractionated accelerated radiotherapy. Clinical Cancer Research Vol. 10, , June 1, McCulloch WS, Pitts W. A logical calculus of the ideas imminent in the nervous activity. Bull Math Biol Vol. 52, , Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-prpagating errors. In: RumelhartDE, McClelland JL, editors. Parallel distributed processing: exploration in the microstructure of cognition. Cambridge, MA: MIT Press; Class notes. ECE: 539, Fall Prof. Hu s matlab programs. Code bp.m: Modified Professor Hu s program which is used for implementation of backpropagation algorithm. ANN.m: This is the main part after preprocessing of the data. This implements and executes the Backpropogation algorithm. This program calls many subroutines from MLP folder and mainly the DataHandler to provide data for MWay Crossvalidation. More detailed comments are available in m file itself. DataHandler.m: Program for M-Way cross validation techniques. More detailed comments are available in m file itself. ProcessData.m: Program to find all locoregional occurrences after 365 days and change the value from 1 to 0(did not occur) as they did not within the 1 year time period which we are looking into. Repeat the same for distant metastasis bptest.m: Modified Professor Hu s program used to get C-mat and C-rate for testing or tuning data for a particular model. ChangeRecords.m: This program replaces the value in a particular record. bpconfig: Modified Professor Hu s program used for configuring the whole model.

8 DeleteRecords: This program deletes records from all parameters for a particular case/index. ReadData: A file with any filename in the format of textdelimited fashion is accepted by this program. The parameter is saved in columns in the order as below. [ki67' pattern' p53' p53int' cd31' bcl2' cyclind' sex' hist' age' t14' nplus' trt' locoreg' distmet'] Other than the above programs, rsample.m, actfun.m, actfunp.m, cvgtest.m, bptest.m, fsplit.m, spline.m, randomize.m,partunef.m and kernel1d.m were used from class website resources.