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INTRODUCTION · AbegailSantillan3,4, Pia Marie Albano2,3,4,b 1Department of Electrical Engineering,...
1
Neural-network-based classifier for breast cancer using FTIR signals obtained from tissues Rock Christian Tomas 1,a *, Anthony Jay Sayat 2 , Andrea Nicole Atienza 2 , Jannah Lianne Danganan 2 , Ma. Rollene Ramos 2 , Allan Fellizar 3,4,5 , Lara Mae Angeles 6,7 , Ruth Bangaoil 3,4 , Abegail Santillan 3,4 , Pia Marie Albano 2,3,4,b 1 Department of Electrical Engineering, University of the Philippines Los Baños, Laguna, Philippines 2 Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines 3 Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines 4 The Graduate School, University of Santo Tomas, Manila, Philippines 5 Mariano Marcos Memorial Hospital and Medical Center, Ilocos Norte, Philippines 6 Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines 7 University of Santo Tomas Hospital , Manila, Philippines INTRODUCTION Breast cancer is the most prevalent type of cancer among women. In most cases, cancer diagnosis relies almost solely on the analysis of H&E-stained biopsies [1] . Due to the volume and the amount of time needed to process biopsy specimens, and the limited number of pathologists around the country, initial diagnoses and subsequent second opinions are often delayed, resulting in the postponement of the needed medical intervention [2] . FUTURE DIRECTIONS Funding Sources CONCLUSION METHODOLOGY *The specimens were derived from a previous study [3] Acquisition of FTIR data , () ∈ , from specimens Preparation of Data Baseline correction of FTIR data, () . Normalization of FTIR data, () . Ԧ : = ) − ( Ԧ ) ( Ԧ RESULTS AND DISCUSSION Z-score normalization via Bruker Optics ALPHA Resolution: 4cm -1 Range: 1850cm -1 to 850cm -1 Method: rubber band method Baseline points: 64 Design of Neural Networks Training and Optimization of Neural Networks Validation and Comparison to other Models Preliminary Analysis: Characterization of FTIR data set Neural Network Design Architecture Diagnostic Performance of Models Model AUC (%) ACC (%) PPV (%) NPV (%) SR (%) RR (%) LDA 71.58 ± 11.84 66.17 ± 10.20 77.67 ± 13.47 55.98 ± 15.34 74.35 ± 11.80 61.56 ± 9.65 LR 86.27 ± 9.58 65.05 ± 12.63 68.83 ± 31.57 60.79 ± 41.06 53.46 ± 32.44 73.00 ± 23.96 NB 66.90 ± 10.90 64.69 ± 10.41 54.44 ± 16.24 77.40 ± 15.09 60.23 ± 9.62 74.22 ± 15.28 DT 69.03 ± 12.83 69.38 ± 11.58 69.56 ± 15.82 69.56 ± 17.37 67.79 ± 13.58 73.19 ± 12.93 RF 84.63 ± 9.30 76.78 ± 9.49 76.94 ± 14.56 76.60 ± 14.88 76.18 ± 12.23 79.91 ± 10.88 SVM 95.72 ± 4.94 90.44 ± 7.78 91.03 ± 9.61 89.77 ± 10.33 90.56 ± 9.68 91.40 ± 8.34 FNN2 90.05 ± 9.95 96.06 ± 7.07 89.83 ± 12.57 94.19 ± 10.57 85.56 ± 18.33 94.34 ± 10.54 FNN4 90.48 ± 10.30 95.54 ± 8.07 91.72 ± 12.06 92.93 ± 12.81 88.38 ± 17.47 92.63 ± 13.31 FNN8 90.35±10.10 95.46±7.55 92.18±11.88 91.85±12.67 89.04±16.75 91.58±13.47 Table 2. Diagnostic performance of feedforward neural networks relative to classical models. Abbreviations: FNN - feed forward neural network; LDA – linear discriminant analysis; NB – Naïve Bayes; DT – decision trees; RF – random forest; SVM – support vector machine; AUC - area under the curve; PPV - positive predictive value, NPV- negative predictive value; SR - specificity rate; RR - recall rate FNN2 FNN4 FNN8 Overall architecture 2 hidden FC layers 350 neurons/layer 4 hidden FC layers 400 neurons/layer 8 hidden FC layers 300 neurons/layer Learning rate 0.01 0.01 0.01 Weight initialization Gaussian random: = 1 , = 0 Cost function Binary cross-entropy Dropout 90% SELU dropout Training Optimizer Stochastic gradient decent (SGD) w/ AdaGrad Epoch 1000 Optimizer: Stochastic Gradient Descent (SGD) Input Layer: Consists of → 483 nodes (absorbance readings) Output Layer: Consists of O N = 2 nodes (probability of malignancy) Architectures: Three (3) FNN (# of hidden layers = {2, 4, 8}) Model Evaluation: 10-fold cross-validation Learning Rate (): Optimized via grid search from 1 – 5x10 -5 Epoch: 1000 Activation Functions: Scaled exponential Linear units () [4] and Parameter Update: AdaGrad – Adaptive Gradient Algorithm Layer Width ( ): Optimized via grid search from 5 - 462 Table 1. Design summary of feedforward neural networks. Figure 3. Optimization surface plot of FNNs. FNN2 FNN4 FNN8 All neural network models were compared to the performance of six (6) machine learning models – logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA) – for benchmarking. The model accuracy based from the validation set was used for selecting the best hyperparameter combination for the optimization. The cross validation procedure was repeated twenty (20) times for each combination to ensure stability. The metrics: area under the curve (AUC), accuracy (ACC), positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR) – were obtained from the test set for each model. The metrics for each model were averaged over 20 trials. Evaluation of FNN model 10-fold cross validation − 70% was used for training, 15% for validation and the last15% for testing. Optimization of and The FNN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics. However, the FNN models were only able to surpass the SVM in terms accuracy, NPV and SR by ~5.24%, ~3.22%, and ~1.45%, respectively. On average, the FNN models performed with an AUC of 90.29% ± 0.22%, an accuracy of 95.68% ± 0.33%, a PPV of 91.25% ± 1.25%, a NPV of 92.99% ± 1.17%, a SR of 87.68% ± 1.82%, and a RR of 92.85% ± 1.39%. Results of the current study show that the NN- based models were able to accurately distinguish between malignant and the benign breast tissues using FTIR data. The NN models were able to outperform the accuracy of the best benchmarking model (SVM) by an average of 5.24%. The significantly high performance metrics of the NN-based models show the potential of neural networks as superior computational tool; specially in cases where the amount of samples considered becomes increasingly large and harder to separate. The findings of the study may have a significant potential for the development of more specific, sensitive, cheaper yet easy-to- perform technique in diagnosing thyroid cancer. Further improvements in the design of the NN- based models is currently being performed by incorporating other relevant patient records such as height, weight, and age as inputs; in order to incorporate causality of the FTIR data to the designs. Analysis of breast cancer variation within the IR functional region is also being considered. A neural network is a mathematical model which is popularly used for high dimension and high density data analysis for classification and regression [4,5] . With the application of AI through NN in the clinical setting, cancer diagnosis may become less time-consuming and more standardized, resulting in a more personalized precision medicine. NN-based diagnostic models will also help reduce inter-observer variability among pathologists with different levels of expertise and years of experience. An emerging alternative diagnostic method involves the use of Fourier transform infrared (FTIR) spectroscopy imaging; where a sample’s absorbance to different wavelengths within the IR spectrum is captured [2,3] . Since the FTIR data describes the chemical fingerprint of a sample tissue, diagnosis may have the potential to detect malignancy before visual changes arise; a limitation of H&E-stained samples. Fourier Transform Infrared Spectroscopy (FTIR) Neural Networks (NN) Objectives of the Study This study aims (1) to design three feedforward neural networks (FNN) of varying layer size, (2) to implement the designed neural network (NN) models through computer simulations and (3) to compare the performance of the designed models with some widely used classifiers. [1] Su KY, Lee WL. Fourier transform infrared spectroscopy as a cancer screening and diagnostic tool: a review and prospects. cancers (basel). 2020;12(1):115. Published 2020 Jan 1. doi:10.3390/cancers12010115. [2] Cohen Y, Zilberman A, Dekel B, Krouk E. Artificial neural network in predicting cancer based on infrared spectroscopy. Int. Conf. on Intelligent Decision Tech (IDT). 2020, pp 141-153. Doi:10.1007/978-981-15-5925-9. [3] Bangaoil R, Santillan A, Angeles LM, Abanilla L, Lim A Jr, Ramos MC, et al. (2020) ATR-FTIR spectroscopy as adjunct method to the microscopic examination of hematoxylin and eosin-stained tissues in diagnosing lung cancer. PLoS ONE 15(5): e0233626. https ://doi.org/10.1371/journal.pone.0233626. [4] Costa F, Marques A, Arnaud-Fassetta G, et al. Self-Normalizing Neural Networks Günter. In: 31st Conf. Neural Inf. Process. Syst. (NIPS ). 2017, pp. 99– 112. [5] Sala A, Anderson DJ, Brennan PM, et al. Biofluid diagnostics by FTIR spectroscopy: A platform technology for cancer detection. Cancer Lett 2020; 477: 122–130. References Figure 1. Mean spectrum of samples. Figure 2. PCA scatter plot of samples. Figure 3. Probability density function of clusters. Figure 1 shows an almost similar FTIR profile for both malignant and benign tissues while Figure 2 visualizes the variation of samples over two most dominant principal components. Lastly, Figure 3 shows the probability density function of the designed LDA classifier. The intersection of the functions supports the visualization in Figure 2 which denotes that the samples are not linearly separable; and hence a polynomial function from more complex models is needed for successful classification.
Transcript of INTRODUCTION · AbegailSantillan3,4, Pia Marie Albano2,3,4,b 1Department of Electrical Engineering,...
Neural-network-based classifier for breast cancer using FTIR signals obtained from tissues
Rock Christian Tomas1,a*, Anthony Jay Sayat2, Andrea Nicole Atienza2, Jannah Lianne Danganan2, Ma. Rollene Ramos2, Allan Fellizar3,4,5, Lara Mae Angeles6,7, Ruth Bangaoil3,4,
Abegail Santillan3,4, Pia Marie Albano2,3,4,b
1Department of Electrical Engineering, University of the Philippines Los Baños, Laguna, Philippines2Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
3Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines4The Graduate School, University of Santo Tomas, Manila, Philippines
5Mariano Marcos Memorial Hospital and Medical Center, Ilocos Norte, Philippines6Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
7University of Santo Tomas Hospital, Manila, Philippines
INTRODUCTION
Breast cancer is the most prevalent type of cancer among women. In most cases, cancer diagnosisrelies almost solely on the analysis of H&E-stained biopsies[1]. Due to the volume and the amount oftime needed to process biopsy specimens, and the limited number of pathologists around thecountry, initial diagnoses and subsequent second opinions are often delayed, resulting in thepostponement of the needed medical intervention[2].
FUTURE DIRECTIONS
Funding Sources
CONCLUSION
METHODOLOGY
*The specimens were derived from a previous study[3]
Acquisition of FTIR
data , 𝒙𝑺𝑷𝑬𝑪(𝒊)
∈ 𝑿𝑺𝑷𝑬𝑪 ,
from specimens
Preparation of Data
Baseline correction of
FTIR data, 𝒙𝑺𝑷𝑬𝑪(𝒊)
.
Normalization of FTIR
data, 𝒙𝑺𝑷𝑬𝑪(𝒊)
.
Ԧ𝑥: =)𝑥 − 𝑚𝑒𝑎𝑛( Ԧ𝑥
)𝑠𝑡𝑑( Ԧ𝑥
RESULTS AND DISCUSSION
Z-score normalizationvia Bruker Optics ALPHAResolution: 4cm-1
Range: 1850cm-1 to 850cm-1
Method: rubber band methodBaseline points: 64
Design of Neural Networks Training and Optimization of
Neural Networks
Validation and Comparison to other Models
Preliminary Analysis: Characterization of FTIR data set
Neural Network Design Architecture
Diagnostic Performance of Models
Model AUC (%) ACC (%) PPV (%) NPV (%) SR (%) RR (%)
LDA 71.58 ± 11.84 66.17 ± 10.20 77.67 ± 13.47 55.98 ± 15.34 74.35 ± 11.80 61.56 ± 9.65
LR 86.27 ± 9.58 65.05 ± 12.63 68.83 ± 31.57 60.79 ± 41.06 53.46 ± 32.44 73.00 ± 23.96
NB 66.90 ± 10.90 64.69 ± 10.41 54.44 ± 16.24 77.40 ± 15.09 60.23 ± 9.62 74.22 ± 15.28
DT 69.03 ± 12.83 69.38 ± 11.58 69.56 ± 15.82 69.56 ± 17.37 67.79 ± 13.58 73.19 ± 12.93
RF 84.63 ± 9.30 76.78 ± 9.49 76.94 ± 14.56 76.60 ± 14.88 76.18 ± 12.23 79.91 ± 10.88
SVM 95.72 ± 4.94 90.44 ± 7.78 91.03 ± 9.61 89.77 ± 10.33 90.56 ± 9.68 91.40 ± 8.34
FNN2 90.05 ± 9.95 96.06 ± 7.07 89.83 ± 12.57 94.19 ± 10.57 85.56 ± 18.33 94.34 ± 10.54
FNN4 90.48 ± 10.30 95.54 ± 8.07 91.72 ± 12.06 92.93 ± 12.81 88.38 ± 17.47 92.63 ± 13.31
FNN8 90.35±10.10 95.46±7.55 92.18±11.88 91.85±12.67 89.04±16.75 91.58±13.47
Table 2. Diagnostic performance of feedforward neural networks relative to classical models.
Abbreviations: FNN - feed forward neural network; LDA – linear discriminant analysis; NB – Naïve Bayes; DT – decision trees; RF – random forest; SVM – support vector machine; AUC - area
under the curve; PPV - positive predictive value, NPV- negative predictive value; SR - specificity rate; RR - recall rate
FNN2 FNN4 FNN8
Overall
architecture
2 hidden FC layers
350 neurons/layer
4 hidden FC layers
400 neurons/layer
8 hidden FC layers
300 neurons/layer
Learning rate 0.01 0.01 0.01
Weight
initializationGaussian random: 𝜎 =
1
𝑖𝑛𝑝𝑢𝑡 𝑠𝑖𝑧𝑒, 𝜇 = 0
Cost function Binary cross-entropy
Dropout 90% SELU dropout
Training
OptimizerStochastic gradient decent (SGD) w/ AdaGrad
Epoch 1000
Optimizer:Stochastic Gradient Descent (SGD)
Input Layer:Consists of 𝑥𝑆𝑃𝐸𝐶 → 483 nodes (absorbance readings)
Output Layer:Consists of ON = 2 nodes (probability of malignancy)
Architectures:Three (3) FNN (# of hidden layers = {2, 4, 8})
Model Evaluation:10-fold cross-validation
Learning Rate (𝑳𝑹):Optimized via gridsearch from 1 – 5x10-5
Epoch: 1000
Activation Functions: Scaled exponential Linear units (𝑆𝐸𝐿𝑈)[4]
and 𝑠𝑜𝑓𝑡𝑚𝑎𝑥
Parameter Update:AdaGrad – Adaptive Gradient Algorithm
Layer Width (𝓁𝒘):Optimized via gridsearch from 5 - 462
Table 1. Design summary of feedforward neural networks.
Figure 3. Optimizationsurface plot of FNNs.
FN
N2
FNN
4FN
N8
All neural network models were compared to the performance of six (6) machine learningmodels – logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF),support vector machine (SVM) and linear discriminant analysis (LDA) – for benchmarking.
The model accuracy based from thevalidation set was used for selecting thebest hyperparameter combination for theoptimization. The cross validationprocedure was repeated twenty (20) timesfor each combination to ensure stability.
The metrics: area under the curve (AUC), accuracy (ACC), positive predictive value (PPV),specificity rate (SR), negative predictive value (NPV), and recall rate (RR) – were obtained fromthe test set for each model. The metrics for each model were averaged over 20 trials.
Evaluation of FNN model
10-fold cross validation − 70% was used fortraining, 15% for validation and the last15%for testing.
Optimization of 𝓵𝒘 and 𝑳𝑹
The FNN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics. However, the FNNmodels were only able to surpass the SVM in terms accuracy, NPV and SR by ~5.24%, ~3.22%, and~1.45%, respectively. On average, the FNN models performed with an AUC of 90.29% ± 0.22%, anaccuracy of 95.68% ± 0.33%, a PPV of 91.25% ± 1.25%, a NPV of 92.99% ± 1.17%, a SR of 87.68% ± 1.82%,and a RR of 92.85% ± 1.39%.
Results of the current study show that the NN-based models were able to accuratelydistinguish between malignant and the benignbreast tissues using FTIR data. The NN modelswere able to outperform the accuracy of thebest benchmarking model (SVM) by anaverage of 5.24%. The significantly highperformance metrics of the NN-based modelsshow the potential of neural networks assuperior computational tool; specially in caseswhere the amount of samples consideredbecomes increasingly large and harder toseparate. The findings of the study may have asignificant potential for the development ofmore specific, sensitive, cheaper yet easy-to-perform technique in diagnosing thyroidcancer.
Further improvements in the design of the NN-based models is currently being performed byincorporating other relevant patient recordssuch as height, weight, and age as inputs; inorder to incorporate causality of the FTIR datato the designs. Analysis of breast cancervariation within the IR functional region is alsobeing considered.
A neural network is a mathematical model which is popularly used for high dimension and highdensity data analysis for classification and regression[4,5]. With the application of AI through NN inthe clinical setting, cancer diagnosis may become less time-consuming and more standardized,resulting in a more personalized precision medicine. NN-based diagnostic models will also helpreduce inter-observer variability among pathologists with different levels of expertise and years ofexperience.
An emerging alternative diagnostic method involves the use of Fourier transform infrared (FTIR)spectroscopy imaging; where a sample’s absorbance to different wavelengths within the IRspectrum is captured[2,3]. Since the FTIR data describes the chemical fingerprint of a sample tissue,diagnosis may have the potential to detect malignancy before visual changes arise; a limitation ofH&E-stained samples.
Fourier Transform Infrared Spectroscopy (FTIR)
Neural Networks (NN)
Objectives of the StudyThis study aims (1) to design three feedforward neural networks (FNN) of varying layer size, (2) toimplement the designed neural network (NN) models through computer simulations and (3) tocompare the performance of the designed models with some widely used classifiers.
[1] Su KY, Lee WL. Fourier transform infrared spectroscopy as a cancerscreening and diagnostic tool: a review and prospects. cancers (basel).2020;12(1):115. Published 2020 Jan 1. doi:10.3390/cancers12010115.
[2] Cohen Y, Zilberman A, Dekel B, Krouk E. Artificial neural network inpredicting cancer based on infrared spectroscopy. Int. Conf. on IntelligentDecision Tech (IDT). 2020, pp 141-153. Doi:10.1007/978-981-15-5925-9.
[3] Bangaoil R, Santillan A, Angeles LM, Abanilla L, Lim A Jr, Ramos MC, et al.(2020) ATR-FTIR spectroscopy as adjunct method to the microscopicexamination of hematoxylin and eosin-stained tissues in diagnosing lungcancer. PLoS ONE 15(5): e0233626.https://doi.org/10.1371/journal.pone.0233626.
[4] Costa F, Marques A, Arnaud-Fassetta G, et al. Self-Normalizing NeuralNetworks Gunter. In: 31st Conf. Neural Inf. Process. Syst. (NIPS ). 2017, pp. 99–112.
[5] Sala A, Anderson DJ, Brennan PM, et al. Biofluid diagnostics by FTIRspectroscopy: A platform technology for cancer detection. Cancer Lett2020; 477: 122–130.
References
Figure 1. Mean spectrum of samples.
Figure 2. PCA scatter plot of samples.
Figure 3. Probability density function of clusters.
Figure 1 shows an almost similar FTIR profile for bothmalignant and benign tissues while Figure 2 visualizes thevariation of samples over two most dominant principalcomponents. Lastly, Figure 3 shows the probability densityfunction of the designed LDA classifier. The intersection ofthe functions supports the visualization in Figure 2 whichdenotes that the samples are not linearly separable; andhence a polynomial function from more complex modelsis needed for successful classification.
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