Development of a Deep Learning Model for the Prognosis of the Occurrence of Death from Stomach Cancer in Senegal — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Development of a Deep Learning Model for the Prognosis of the Occurrence of Death from Stomach Cancer in Senegal
Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Computing and Management, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Computing and Management, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Civil Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Mathematics, Alioune Diop University of Bambey (UADB), Bambey, Senegal
,
Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
,
Department of Surgery and Surgical Specialties, Faculty of Medicine, Pharmacy and Odonto-Stomatology, Cheikh Anta Diop University of Dakar (UCAD), Dakar, Senegal
1 Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
2 Department of Computing and Management, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
3 Department of Computing and Management, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
4 Department of Civil Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
5 Department of Mathematics, Alioune Diop University of Bambey (UADB), Bambey, Senegal
6 Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
7 Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of Thiès (UIDT), Thiès, Senegal
8 Department of Surgery and Surgical Specialties, Faculty of Medicine, Pharmacy and Odonto-Stomatology, Cheikh Anta Diop University of Dakar (UCAD), Dakar, Senegal
Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develop a machine-learning model for the prognosis of death from stomach cancer 5 years after treatment. Methods: Our study sample consisted of 262 patients treated for gastric cancer at Aristide le Dantec Hospital and followed postoperatively between 2007 and 2020. We developed a multilayer perceptron with optimal hyperparameters and compared its performance with standard classification algorithms. We also augmented our data with a set of synthetic data generators to evaluate the behaviour of the model when faced with a larger amount of data. Results: Our model obtained an accuracy of 97.5%, outperforming the SVM (93%), RF (93.8%) and KNN (92.7%) models. An improvement of 1.5% in accuracy was achieved with synthetic data. Our study showed that the most pejorative factors in the evolution of the cancer were the appearance of hepatic metastases or adenopathy, smoking, and the infiltrative and stenosing aspects of the tumour on endoscopy. Conclusion: Our model predicted the occurrence of death from gastric cancer with very high accuracy, outperforming standard classification algorithms. The increase in training data produced an improvement in accuracy. Our study will help doctors to personalize the management of gastric cancer patients.
KeywordsArtificial IntelligenceMultilayer PerceptronPrognosisGastric CancerSynthetic Data
(2022) OMS: Organisation Mondiale de la santé. https://www.who.int
(2020) GCO: Global Cancer Observation. https://gco.iarc.fr
Sylla, M., Ossibi, P.E., Soumana, I.D., Souiki, T., Toughrai, I., Ousadden, A., et al . (2020) Etudes des facteurs histo-pronostiques des cancers gastriques opérés au Centre Hospitalo-Universitaire de Fès. PAMJ Clinical Medicine , 4, Article 54. https://doi.org/10.11604/pamj-cm.2020.4.54.23112
Dinh-Xuan, A. (2019) Artificial Intelligence, Machine Learning and Deep Learning: New Concepts and Future Key Players in Respiratory Medicine. Revue des Maladies Respiratoires Actualités , 11, 59-62. https://doi.org/10.1016/s1877-1203(19)30031-x
Voarino, N. (2020) Systèmes d’intelligence artificielle et santé: Les enjeux d’une innovation responsable. Ph.D. Thesis, Université de Montréal. https://doi.org/1866/23526
Bisong, E. (2019) The Multilayer Perceptron (MLP). In: Bisong, E., Ed., Building Machine Learning and Deep Learning Models on Google Cloud Platform , Apress, 401-405. https://doi.org/10.1007/978-1-4842-4470-8_31
Que, S., Chen, Q., Zhong, Q., Liu, Z., Wang, J., Lin, J., et al . (2019) Application of Preoperative Artificial Neural Network Based on Blood Biomarkers and Clinicopathological Parameters for Predicting Long-Term Survival of Patients with Gastric Cancer. World Journal of Gastroenterology , 25, 6451-6464. https://doi.org/10.3748/wjg.v25.i43.6451
Hao, D., Li, Q., Feng, Q., Qi, L., Liu, X., Arefan, D., et al . (2022) Survivalcnn: A Deep Learning-Based Method for Gastric Cancer Survival Prediction Using Radiological Imaging Data and Clinicopathological Variables. Artificial Intelligence in Medicine , 134, Article ID: 102424. https://doi.org/10.1016/j.artmed.2022.102424
Huang, B., Tian, S., Zhan, N., Ma, J., Huang, Z., Zhang, C., et al . (2021) Accurate Diagnosis and Prognosis Prediction of Gastric Cancer Using Deep Learning on Digital Pathological Images: A Retrospective Multicentre Study. eBioMedicine , 73, Article ID: 103631. https://doi.org/10.1016/j.ebiom.2021.103631
Wang, S., Dong, D., Zhang, W., Hu, H., Li, H., Zhu, Y., et al . (2021) Specific Borrmann Classification in Advanced Gastric Cancer by an Ensemble Multilayer Perceptron Network: A Multicenter Research. Medical Physics , 48, 5017-5028. https://doi.org/10.1002/mp.15094
Li, Z., Wu, X., Gao, X., Shan, F., Ying, X., Zhang, Y., et al . (2020) Development and Validation of an Artificial Neural Network Prognostic Model after Gastrectomy for Gastric Carcinoma: An International Multicenter Cohort Study. Cancer Medicine , 9, 6205-6215. https://doi.org/10.1002/cam4.3245
Wu, L., Zhou, W., Wan, X., Zhang, J., Shen, L., Hu, S., et al . (2019) A Deep Neural Network Improves Endoscopic Detection of Early Gastric Cancer without Blind Spots. Endoscopy , 51, 522-531. https://doi.org/10.1055/a-0855-3532
Nakahira, H., Ishihara, R., Aoyama, K., Kono, M., Fukuda, H., Shimamoto, Y., et al . (2019) Stratification of Gastric Cancer Risk Using a Deep Neural Network. JGH Open , 4, 466-471. https://doi.org/10.1002/jgh3.12281
Dastres, R. and Soori, M. (2021) Artificial Neural Network Systems. International Journal of Imaging and Robotics ( IJIR ), 21, 13-25.
Nayarisseri, A. (2021) Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Current Drug Targets , 22, 631-655. https://doi.org/10.2174/18735592mtezsmdmnz
Hernandez, M., Epelde, G., Beristain, A., Álvarez, R., Molina, C., Larrea, X., et al . (2022) Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain. Electronics , 11, Article 812. https://doi.org/10.3390/electronics11050812
Chen, Y., Yang, X., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., et al . (2022) Generative Adversarial Networks in Medical Image Augmentation: A Review. Computers in Biology and Medicine , 144, Article ID: 105382. https://doi.org/10.1016/j.compbiomed.2022.105382
Kanayama, Teppei, et al . (2019) Gastric Cancer Detection from Endoscopic Images Using Synthesis by GAN. In: Shen, D., et al . Eds., Medical Image Computing and Computer Assisted Intervention — MICCAI 2019, Springer, 530-538.
Lin, H., Liu, Y., Li, S. and Qu, X. (2023) How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey. IEEE / CAA Journal of Automatica Sinica , 10, 1781-1796. https://doi.org/10.1109/jas.2023.123744
Kingma, D.P. and Welling, M. (2019) An Introduction to Variational Autoencoders. Foundations and Trends® in Machine Learning , 12, 307-392. https://doi.org/10.1561/2200000056
Antoniou, A., Storkey, A. and Edwards, H. (2017) Data Augmentation Generative Ad-versarial Networks. arXiv: 1711.04340. https://doi.org/10.48550/arXiv.1711.04340
Wei, Q. and Ramsey, S.A. (2021) Predicting Chemotherapy Response Using a Variational Autoencoder Approach. BMC Bioinformatics , 22, Article No. 453. https://doi.org/10.1186/s12859-021-04339-6
Li, F., Li, J., Liu, L., Huang, L., Zhou, L. and He, H. (2023) Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay. Remote Sensing , 15, Article 4824. https://doi.org/10.3390/rs15194824
Nelli, F. (2023) Machine Learning with Scikit-Learn. In: Nelli, F., Ed., Python Data Analytics : With Pandas , Numpy , and Matplotlib , Apress, 259-287. https://doi.org/10.1007/978-1-4842-9532-8_8
Berrar, D. (2019) Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology , 1, 542-545. https://doi.org/10.1016/b978-0-12-809633-8.20349-x
YData (2024) Synthetic Data Generation. https://ydata.ai/products/synthetic_data
Mostly.AI (2024) Synthetic Data Generation. https://mostly.ai/features
Synthesized.io (2024) Synthetic Data Generation. https://www.synthesized.io
Gretel.AI (2024) Synthetic Data Generation. https://gretel.ai/synthetics
Copulas (2024) Synthetic Data Generation. https://github.com/sdv-dev/Copulas
Twinify (2024) Synthetic Data Generation. https://github.com/DPBayes/twinify
Benerator (2024) Synthetic Data Generation. https://github.com/rapiddweller/rapiddweller-benerator-ce
Thierry, F. (1991) Introduction aux Tests Statistiques. Editions Technip.
Gebreyesus, Y., Dalton, D., Nixon, S., De Chiara, D. and Chinnici, M. (2023) Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive Explanation (SHAP). Future Internet , 15, Article 88. https://doi.org/10.3390/fi15030088
Xie, F., Huang, B., Chen, Z., Cai, R., Glymour, C., Geng, Z. and Zhang, K. (2023) Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. arXiv: 2308.06718. https://doi.org/10.48550/arXiv.2308.06718
Tsagris, M. (2018) Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence , 33, 101-123. https://doi.org/10.1080/08839514.2018.1526760
Dieng, M., Savadogo, K.A.O., Konate, I., Cisse, M., Manyancka, M.A., Nyemb, P., Fall, B., Dia, A. and Toure, C.T. (2010) Traitement Chirurgical De L’adénocarcinome Gastrique Au Chu De Dakar 1995 À 2005. Journal Africain de chirurgie digestive , 10, 1059-1062.
Bang, G.A., Savom, E.P., Oumarou, B.N. and Ngamy, C.K. (2020) Epidémiologie clinique et facteurs de risque de mortalité du cancer gastrique en Afrique subsaharienne: Analyse rétrospective de 120 cas à Yaoundé (Cameroun). Pan African Medical Journal , 37, Article 104.
Tamegnon, D., Rafiou, E.Y., Komlan, A., Ayi, A., Olivier, A.E., et al. (2022) Stomach Cancer: Epidemiological, Diagnostic and Therapeutic Aspects at the Kara Teaching Hospital, Togo. Archives of Surgery and Clinical Research , 6, 001-003. https://doi.org/10.29328/journal.ascr.1001062
Neto, C., Brito, M., Lopes, V., Peixoto, H., Abelha, A. and Machado, J. (2019) Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients. Entropy , 21, Article 1163. https://doi.org/10.3390/e21121163
Diop, B., Dia, A.A., Ba, P.A., Sow, O., Thiam, O., Konate, I., Dieng, M. and Sarre, S.M. (2017) Prise en Charge Chirurgicale des Tumeurs Gastriques à Dakar: À Propos de 36 Observations. Health Sciences and Disease , 18, 34-38.
Rosen, R.D. and Sapra, A. (2022) Classement TNM. National Institute of Health. https://www.ncbi.nlm.nih.gov/books/NBK553187/
Hassani, A., Mahfoud, W., Sadaoui, I., Fechtali, T., Benomar, H. and Benomar, H. (2020) Étude des caractéristiques épidémiologiques cliniques et anatomopathologiques de l’adénocarcinome gastrique chez une population Marocaine. Annales de Pathologie , 40, 442-446. https://doi.org/10.1016/j.annpat.2020.04.014
Zhang, S. (2022) Challenges in KNN Classification. IEEE Transactions on Knowledge and Data Engineering , 34, 4663-4675. https://doi.org/10.1109/tkde.2021.3049250
Abdullah, M.D. and Abdulazeez, M.A. (2021) Machine Learning Applications Based on SVM Classification a Review. Qubahan Academic Journal , 1, 81-90. https://doi.org/10.48161/qaj.v1n2a50
Rigatti, S.J. (2017) Random Forest. Journal of Insurance Medicine , 47, 31-39. https://doi.org/10.17849/insm-47-01-31-39.1
Sy, I., Bousso, M., Correa, A., Loum, M., Diop, A., Toure, K., et al . (2022) Study of Prognostic Factors in Gastric Cancer: Application of a Cox Model and Logistic Regression. International Journal of Statistics and Applied Mathematics , 7, 108-113. https://doi.org/10.22271/maths.2022.v7.i5b.887
Fan, Z., Guo, Y., Gu, X., Huang, R. and Miao, W. (2022) Development and Validation of an Artificial Neural Network Model for Non-Invasive Gastric Cancer Screening and Diagnosis. Scientific Reports , 12, Article No. 21795. https://doi.org/10.1038/s41598-022-26477-4
Xue, Z., Lu, J., Lin, J., Huang, C.M., Li, P., Xie, J.W., Wang, J.B., Lin, J.X., Chen, Q.Y. and Zheng, C.H. (2022) [Establishment of Artificial Neural Network Model for Predicting Lymph Node Metastasis in Patients with Stage II-III Gastric Cancer]. Chinese Journal of Gastrointestinal Surgery , 25, 327-335. (In Chinese) https://doi.org/10.3760/cma.j.cn441530-20220105-00010
Nabati, M., Navidan, H., Shahbazian, R., Ghorashi, S.A. and Windridge, D. (2020) Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach. IEEE Sensors Letters , 4, 1-4. https://doi.org/10.1109/lsens.2020.2971555
Bhinder, B., Gilvary, C., Madhukar, N.S. and Elemento, O. (2021) Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discovery , 11, 900-915. https://doi.org/10.1158/2159-8290.cd-21-0090
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. (2017) Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision ( ICCV ), Venice, 22-29 October 2017, 618-626. https://doi.org/10.1109/iccv.2017.74
Granieri, S., Altomare, M., Bruno, F., et al. (2021) Surgical Treatment of Gastric Cancer Liver Metastases: Systematic Review and Meta-Analysis of Long-Term Outcomes and Prognostic Factors. Critical Reviews in Oncology/Hematology , 163, 103313. https://doi.org/10.1016/j.critrevonc.2021.103313
Eom, B.W., Jung, K., Won, Y., Yang, H. and Kim, Y. (2018) Tendances de l’incidence du cancer gastrique selon les caractéristiques clinicopathologiques en Corée 1999-2014. Cancer Research and Treatment , 50, 1343-1350. https://doi.org/10.4143/crt.2017.464
Koura, M., Ollo, R., Ouattara, D.Z. and Zongo, P. (2019) Stomach Cancer in a Sub-Saharan African Country: Epidemiological, Anatomoclinical and Endoscopic Aspects in Bobo-Dioulasso (Burkina Faso). African Jou r nals Online , 42, 79-86.
Berkane, M.A. and Sadouki, M. (2024) Interest of Perioperative Chemotherapy Type Flot (5-Fluorouracil, Oxaliplatin, Docetaxel) in the Management of Non-Metastatic Gastric Cancer. Master’s Thesis, University of Constantine 3—Salah Boubnider. https://dspace.univ-constantine3.dz/jspui/handle/123456789/5709
Benhamada, R. and Makhloufi, S. (2022) Surgery and combined Chemotherapy in the Treatment of Gastric Cancer. Master’s Thesis, University of Constantine 3—Salah Boubnider. https://dspace.univ-constantine3.dz/jspui/handle/123456789/3363
Sy, I., Bousso, M., Correa, A. and Dieng, M. (2023) State of the Art of Artificial Intelligence Applications in Oncology. Open Journal of Applied Sciences , 13, 2245-2262. https://doi.org/10.4236/ojapps.2023.1312175