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.