CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer
Objective: The objective of the study is to evaluate the performance of CNN-based proposed models for predicting patients' response to NAC treatment and the disease development process in the pathological area. The study aims to determine the main criteria that affect the model's success d...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023040197 |
| _version_ | 1797813733494358016 |
|---|---|
| author | Yasin Kirelli Seher Arslankaya Havva Belma Koçer Tarık Harmantepe |
| author_facet | Yasin Kirelli Seher Arslankaya Havva Belma Koçer Tarık Harmantepe |
| author_sort | Yasin Kirelli |
| collection | DOAJ |
| description | Objective: The objective of the study is to evaluate the performance of CNN-based proposed models for predicting patients' response to NAC treatment and the disease development process in the pathological area. The study aims to determine the main criteria that affect the model's success during training, such as the number of convolutional layers, dataset quality and depended variable. Method: The study uses pathological data frequently used in the healthcare industry to evaluate the proposed CNN-based models. The researchers analyze the classification performances of the models and evaluate their success during training. Results: The study shows that using deep learning methods, particularly CNN models, can offer strong feature representation and lead to accurate predictions of patients' response to NAC treatment and the disease development process in the pathological area. A model that predicts ‘miller coefficient’, ‘tumor lymph node value’, ‘complete response in both tumor and axilla’ values with high accuracy, which is considered to be effective in achieving complete response to treatment, has been created. Estimation performance metrics have been obtained as 87%, 77% and 91%, respectively. Conclusion: The study concludes that interpreting pathological test results with deep learning methods is an effective way of determining the correct diagnosis and treatment method, as well as the prognosis follow-up of the patient. It provides clinicians with a solution to a large extent, particularly in the case of large, heterogeneous datasets that can be challenging to manage with traditional methods. The study suggests that using machine learning and deep learning methods can significantly improve the performance of interpreting and managing healthcare data. |
| first_indexed | 2024-03-13T07:57:08Z |
| format | Article |
| id | doaj.art-7c1d54c3cf4b42d8b477f06907c3f3f8 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T07:57:08Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-7c1d54c3cf4b42d8b477f06907c3f3f82023-06-02T04:23:39ZengElsevierHeliyon2405-84402023-06-0196e16812CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancerYasin Kirelli0Seher Arslankaya1Havva Belma Koçer2Tarık Harmantepe3Management Information Systems, Kutahya Dumlupinar University, Kutahya, Turkey; Corresponding author.Industrial Engineering Department, Sakarya University, Sakarya, TurkeyMedicine, Sakarya University, Sakarya, TurkeyMedicine, Sakarya University, Sakarya, TurkeyObjective: The objective of the study is to evaluate the performance of CNN-based proposed models for predicting patients' response to NAC treatment and the disease development process in the pathological area. The study aims to determine the main criteria that affect the model's success during training, such as the number of convolutional layers, dataset quality and depended variable. Method: The study uses pathological data frequently used in the healthcare industry to evaluate the proposed CNN-based models. The researchers analyze the classification performances of the models and evaluate their success during training. Results: The study shows that using deep learning methods, particularly CNN models, can offer strong feature representation and lead to accurate predictions of patients' response to NAC treatment and the disease development process in the pathological area. A model that predicts ‘miller coefficient’, ‘tumor lymph node value’, ‘complete response in both tumor and axilla’ values with high accuracy, which is considered to be effective in achieving complete response to treatment, has been created. Estimation performance metrics have been obtained as 87%, 77% and 91%, respectively. Conclusion: The study concludes that interpreting pathological test results with deep learning methods is an effective way of determining the correct diagnosis and treatment method, as well as the prognosis follow-up of the patient. It provides clinicians with a solution to a large extent, particularly in the case of large, heterogeneous datasets that can be challenging to manage with traditional methods. The study suggests that using machine learning and deep learning methods can significantly improve the performance of interpreting and managing healthcare data.http://www.sciencedirect.com/science/article/pii/S2405844023040197Artificial intelligence in healthDeep learningCNNNeoadjuvant chemotherapy |
| spellingShingle | Yasin Kirelli Seher Arslankaya Havva Belma Koçer Tarık Harmantepe CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer Heliyon Artificial intelligence in health Deep learning CNN Neoadjuvant chemotherapy |
| title | CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer |
| title_full | CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer |
| title_fullStr | CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer |
| title_full_unstemmed | CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer |
| title_short | CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer |
| title_sort | cnn based deep learning method for predicting the disease response to the neoadjuvant chemotherapy nac treatment in breast cancer |
| topic | Artificial intelligence in health Deep learning CNN Neoadjuvant chemotherapy |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023040197 |
| work_keys_str_mv | AT yasinkirelli cnnbaseddeeplearningmethodforpredictingthediseaseresponsetotheneoadjuvantchemotherapynactreatmentinbreastcancer AT seherarslankaya cnnbaseddeeplearningmethodforpredictingthediseaseresponsetotheneoadjuvantchemotherapynactreatmentinbreastcancer AT havvabelmakocer cnnbaseddeeplearningmethodforpredictingthediseaseresponsetotheneoadjuvantchemotherapynactreatmentinbreastcancer AT tarıkharmantepe cnnbaseddeeplearningmethodforpredictingthediseaseresponsetotheneoadjuvantchemotherapynactreatmentinbreastcancer |