Interpretation of an explainable artificial intelligence model for the diagnosis of breast cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHU Zhongjian ^1,2 , LI Tianyuan ^1,2 ,  DU Zhenggui ^1,2

1. Breast Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

DU Zhenggui, Email: docduzg@163.com

Keywords：

Breast cancer; explainable artificial intelligence; diagnosis; interpretation

DOI：

10.7507/1007-4848.202503093

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Pathological diagnosis is the gold standard for confirming breast cancer. Traditional manual pathological diagnosis methods for breast cancer are time-consuming, labor-intensive, highly subjective, and exhibit poor diagnostic consistency. In recent years, artificial intelligence (AI) technology has rapidly advanced and is progressively being applied clinically as a promising early diagnostic tool. However, many existing AI models lack interpretability, which limits the trustworthiness of their clinical application. Khater et al, by combining a high-precision machine learning model with an explainable AI model, achieved highly accurate breast tumor diagnosis and provided explanations for key biological and pathological features influencing the diagnostic results. This points the way for the future application and development of AI in medical diagnosis and treatment. The article interprets the main content of that study, and analyzes the advantages and limitations of AI in medical diagnosis and treatment, with the aim of promoting its better application in clinical practice.

1.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	Sun K, Zhang B, Lei S, et al. Incidence, mortality, and disability-adjusted life years of female breast cancer in China, 2022. Chin Med J (Engl), 2024, 137(20): 2429-2436.
3.	赫捷, 陈万青, 李霓, 等. 中国女性乳腺癌筛查与早诊早治指南 (2021, 北京). 中国肿瘤, 2021, 30(3): 161-191.He J, Chen WQ, Li N, et al. China guideline for the screening and early detection of female breast cancer (2021, Beijing). China Cancer, 2021, 30(3): 161-191.
4.	Tang DD, Ye ZJ, Liu WW, et al. Survival feature and trend of female breast cancer: A comprehensive review of survival analysis from cancer registration data. Breast, 2025, 79: 103862.
5.	Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA, 2015, 313(11): 1122-1132.
6.	国家肿瘤质控中心乳腺癌专家委员会, 中国抗癌协会乳腺癌专业委员会, 中国抗癌协会肿瘤药物临床研究专业委员会. 中国晚期乳腺癌规范诊疗指南 (2024版). 中华肿瘤杂志, 2024, 46(12): 1079-1106.Breast Cancer Expert Committee of National Cancer Quality Control Center, Breast Cancer Expert Committee of China Anti-Cancer Association, Cancer Drug Clinical Research Committee of China Anti-Cancer Association. Guidelines for diagnosis and treatment of advanced breast cancer in China (2024 edition). Chin J Oncol, 2024, 46(12): 1079-1106.
7.	Kaddes M, Ayid YM, Elshewey AM, et al. Breast cancer classification based on hybrid CNN with LSTM model. Sci Rep, 2025, 15(1): 4409.
8.	McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
9.	Retamero JA, Gulturk E, Bozkurt A, et al. Artificial intelligence helps pathologists increase diagnostic accuracy and efficiency in the detection of breast cancer lymph node metastases. Am J Surg Pathol, 2024, 48(7): 846-854.
10.	Lami K, Yoon HS, Parwani AV, et al. Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: A retrospective study with a Japanese cohort. Pathology, 2024, 56(5): 633-642.
11.	Challa B, Tahir M, Hu Y, et al. Artificial intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow. Mod Pathol, 2023, 36(8): 100216.
12.	Khater T, Hussain A, Bendardaf R, et al. An explainable artificial intelligence model for the classification of breast cancer. IEEE Access, 2025, 13: 5618-5633.
13.	Wolberg WH, Mangasarian OL. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Natl Acad Sci U S A, 1990, 87(23): 9193-9196.
14.	Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. In: Proceedings of the Biomedical Image Processing and Biomedical Visualization. Bellingham, WA: SPIE, 1993.
15.	De Cecco LG, Aidini A. Explainable AI with Python. Cham, Switzerland: Springer, 2021.
16.	Kumar SL, Munish K, Rekha S. Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Adv Eng Softw, 2023, 175: 103234.
17.	Saad A, Manimurugan S, Byung-Gyu K, et al. Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO). Appl Soft Comput, 2023, 142: 110488.
18.	Li H, Wang S, Zeng Q, et al. Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer. Photodiagnosis Photodyn Ther, 2022, 40: 103115.
19.	Martiniussen MA, Larsen M, Hovda T, et al. Performance of two deep learning-based AI models for breast cancer detection and localization on screening mammograms from breastscreen norway. Radiol Artif Intell, 2025, 7(3): e240039.
20.	Mazer BL, Homer RJ, Rimm DL. False-positive pathology: Improving reproducibility with the next generation of pathologists. Lab Invest, 2019, 99(9): 1260-1265.
21.	Elmore JG, Barnhill RL, Elder DE, et al. Pathologists' diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study. BMJ, 2017, 357: j2813.
22.	Jackson SL, Frederick PD, Pepe MS, et al. Diagnostic reproducibility: What happens when the same pathologist interprets the same breast biopsy specimen at two points in time? Ann Surg Oncol, 2017, 24(5): 1234-1241.
23.	Stolz M. The revolution in breast cancer diagnostics: From visual inspection of histopathology slides to using desktop tissue analysers for automated nanomechanical profiling of tumours. Bioengineering (Basel), 2024, 11(3): 237.
24.	石慧娟, 刘旭斌, 康继辉, 等. 开展临床病理 "一对一" 教学培养独立行医的病理医生. 中国毕业后医学教育, 2020, 4(5): 435-438.Shi HJ, Liu XB, Kang JH, et al. Pathology doctors capable of independent practice trained by one-to-one teaching in clinic pathology. Chin J Grad Med Educ, 2020, 4(5): 435-438.
25.	Walsh E, Orsi NM. The current troubled state of the global pathology workforce: A concise review. Diagn Pathol, 2024, 19(1): 163.
26.	Wolfe C, Phillips R, Laheru D, Fisher R. DP02 (P30) declining staff numbers and increasing workload: Is there a solution? Br J Dermatol, 2023, 188(Suppl 4): ljad113.
27.	Savala R, Dey P, Gupta N. Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid. Diagn Cytopathol, 2018, 46(3): 244-249.
28.	Chang YH, Shin CM, Lee HD, et al. Real-world application of artificial intelligence for detecting pathologic gastric atypia and neoplastic lesions. J Gastric Cancer, 2024, 24(3): 327-340.
29.	Ruan J, Xu S, Chen R, et al. EMLI-ICC: An ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma. Brief Bioinform, 2022, 23(6): bbac450.
30.	Wang X, Zhao J, Marostica E, et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature, 2024, 634(8035): 970-978.
31.	Silva-Aravena F, Núñez Delafuente H, Gutiérrez-Bahamondes JH, et al. A hybrid algorithm of ML and XAI to prevent breast cancer: A strategy to support decision making. Cancers (Basel), 2023, 15(9): 2443.
32.	Kumar S, Das A. Peripheral blood mononuclear cell derived biomarker detection using explainable artificial intelligence (XAI) provides better diagnosis of breast cancer. Comput Biol Chem, 2023, 104: 107867.
33.	Al-Antari MA, Al-Masni MA, Choi MT, et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform, 2018, 117: 44-54.
34.	Rabiei R, Ayyoubzadeh SM, Sohrabei S, et al. Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng, 2022, 12(3): 297-308.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Sun K, Zhang B, Lei S, et al. Incidence, mortality, and disability-adjusted life years of female breast cancer in China, 2022. Chin Med J (Engl), 2024, 137(20): 2429-2436.
3. 赫捷, 陈万青, 李霓, 等. 中国女性乳腺癌筛查与早诊早治指南 (2021, 北京). 中国肿瘤, 2021, 30(3): 161-191.He J, Chen WQ, Li N, et al. China guideline for the screening and early detection of female breast cancer (2021, Beijing). China Cancer, 2021, 30(3): 161-191.
4. Tang DD, Ye ZJ, Liu WW, et al. Survival feature and trend of female breast cancer: A comprehensive review of survival analysis from cancer registration data. Breast, 2025, 79: 103862.
5. Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA, 2015, 313(11): 1122-1132.
6. 国家肿瘤质控中心乳腺癌专家委员会, 中国抗癌协会乳腺癌专业委员会, 中国抗癌协会肿瘤药物临床研究专业委员会. 中国晚期乳腺癌规范诊疗指南 (2024版). 中华肿瘤杂志, 2024, 46(12): 1079-1106.Breast Cancer Expert Committee of National Cancer Quality Control Center, Breast Cancer Expert Committee of China Anti-Cancer Association, Cancer Drug Clinical Research Committee of China Anti-Cancer Association. Guidelines for diagnosis and treatment of advanced breast cancer in China (2024 edition). Chin J Oncol, 2024, 46(12): 1079-1106.
7. Kaddes M, Ayid YM, Elshewey AM, et al. Breast cancer classification based on hybrid CNN with LSTM model. Sci Rep, 2025, 15(1): 4409.
8. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
9. Retamero JA, Gulturk E, Bozkurt A, et al. Artificial intelligence helps pathologists increase diagnostic accuracy and efficiency in the detection of breast cancer lymph node metastases. Am J Surg Pathol, 2024, 48(7): 846-854.
10. Lami K, Yoon HS, Parwani AV, et al. Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: A retrospective study with a Japanese cohort. Pathology, 2024, 56(5): 633-642.
11. Challa B, Tahir M, Hu Y, et al. Artificial intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow. Mod Pathol, 2023, 36(8): 100216.
12. Khater T, Hussain A, Bendardaf R, et al. An explainable artificial intelligence model for the classification of breast cancer. IEEE Access, 2025, 13: 5618-5633.
13. Wolberg WH, Mangasarian OL. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Natl Acad Sci U S A, 1990, 87(23): 9193-9196.
14. Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. In: Proceedings of the Biomedical Image Processing and Biomedical Visualization. Bellingham, WA: SPIE, 1993.
15. De Cecco LG, Aidini A. Explainable AI with Python. Cham, Switzerland: Springer, 2021.
16. Kumar SL, Munish K, Rekha S. Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Adv Eng Softw, 2023, 175: 103234.
17. Saad A, Manimurugan S, Byung-Gyu K, et al. Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO). Appl Soft Comput, 2023, 142: 110488.
18. Li H, Wang S, Zeng Q, et al. Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer. Photodiagnosis Photodyn Ther, 2022, 40: 103115.
19. Martiniussen MA, Larsen M, Hovda T, et al. Performance of two deep learning-based AI models for breast cancer detection and localization on screening mammograms from breastscreen norway. Radiol Artif Intell, 2025, 7(3): e240039.
20. Mazer BL, Homer RJ, Rimm DL. False-positive pathology: Improving reproducibility with the next generation of pathologists. Lab Invest, 2019, 99(9): 1260-1265.
21. Elmore JG, Barnhill RL, Elder DE, et al. Pathologists' diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study. BMJ, 2017, 357: j2813.
22. Jackson SL, Frederick PD, Pepe MS, et al. Diagnostic reproducibility: What happens when the same pathologist interprets the same breast biopsy specimen at two points in time? Ann Surg Oncol, 2017, 24(5): 1234-1241.
23. Stolz M. The revolution in breast cancer diagnostics: From visual inspection of histopathology slides to using desktop tissue analysers for automated nanomechanical profiling of tumours. Bioengineering (Basel), 2024, 11(3): 237.
24. 石慧娟, 刘旭斌, 康继辉, 等. 开展临床病理 "一对一" 教学培养独立行医的病理医生. 中国毕业后医学教育, 2020, 4(5): 435-438.Shi HJ, Liu XB, Kang JH, et al. Pathology doctors capable of independent practice trained by one-to-one teaching in clinic pathology. Chin J Grad Med Educ, 2020, 4(5): 435-438.
25. Walsh E, Orsi NM. The current troubled state of the global pathology workforce: A concise review. Diagn Pathol, 2024, 19(1): 163.
26. Wolfe C, Phillips R, Laheru D, Fisher R. DP02 (P30) declining staff numbers and increasing workload: Is there a solution? Br J Dermatol, 2023, 188(Suppl 4): ljad113.
27. Savala R, Dey P, Gupta N. Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid. Diagn Cytopathol, 2018, 46(3): 244-249.
28. Chang YH, Shin CM, Lee HD, et al. Real-world application of artificial intelligence for detecting pathologic gastric atypia and neoplastic lesions. J Gastric Cancer, 2024, 24(3): 327-340.
29. Ruan J, Xu S, Chen R, et al. EMLI-ICC: An ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma. Brief Bioinform, 2022, 23(6): bbac450.
30. Wang X, Zhao J, Marostica E, et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature, 2024, 634(8035): 970-978.
31. Silva-Aravena F, Núñez Delafuente H, Gutiérrez-Bahamondes JH, et al. A hybrid algorithm of ML and XAI to prevent breast cancer: A strategy to support decision making. Cancers (Basel), 2023, 15(9): 2443.
32. Kumar S, Das A. Peripheral blood mononuclear cell derived biomarker detection using explainable artificial intelligence (XAI) provides better diagnosis of breast cancer. Comput Biol Chem, 2023, 104: 107867.
33. Al-Antari MA, Al-Masni MA, Choi MT, et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform, 2018, 117: 44-54.
34. Rabiei R, Ayyoubzadeh SM, Sohrabei S, et al. Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng, 2022, 12(3): 297-308.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesInterpretation of an explainable artificial intelligence model for the diagnosis of breast cancer

Abstract Full text Figures/Tables Video References Cited by

Format

Content