Journal of Surgery Concepts & Practice ›› 2024, Vol. 29 ›› Issue (05): 389-395.doi: 10.16139/j.1007-9610.2024.05.04
• Experts forum • Previous Articles Next Articles
DA Qian, RUAN Miao,*, FEI Xiaochun, WANG Chaofu()
Received:
2024-08-12
Online:
2024-09-25
Published:
2025-01-23
Contact:
WANG Chaofu
E-mail:wcf11956@rjh.com.cn
CLC Number:
DA Qian, RUAN Miao, FEI Xiaochun, WANG Chaofu. Application and research prospects of artificial intelligence in breast cancer pathological diagnosis[J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 389-395.
[7] |
BERA K, SCHALPER K A, RIMM D L, et al. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology[J]. Nat Rev Clin Oncol, 2019, 16(11):703-715.
doi: 10.1038/s41571-019-0252-y pmid: 31399699 |
[8] | CHENG J, REN C, LIU G, et al. Development of high-resolution dedicated PET-based radiomics machine learning model to predict axillary lymph node status in early-stage breast cancer[J]. Cancers (Basel), 2022, 14(4):950. |
[9] |
CAMPANELLA G, HANNA M G, GENESLAW L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images[J]. Nat Med, 2019, 25(8):1301-1309.
doi: 10.1038/s41591-019-0508-1 pmid: 31308507 |
[10] | JACKSON H W, FISCHER J R, ZANOTELLI V R T, et al. The single-cell pathology landscape of breast cancer[J]. Nature, 2020, 578(7796):615-620. |
[11] | LIU M, HU L, TANG Y, et al. A deep learning method for breast cancer classification in the pathology images[J]. IEEE J Biomed Health Inform, 2022, 26(10):5025-5032. |
[12] | ZHANG T, TAN T, WANG X, et al. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease[J]. Cell Rep Med, 2023, 4(8):101131. |
[13] |
NAM S, CHONG Y, JUNG CK, et al. Introduction to digital pathology and computer-aided pathology[J]. J Pathol Transl Med, 2020, 54(2):125-134.
doi: 10.4132/jptm.2019.12.31 pmid: 32045965 |
[14] | SANDERS M E, SCHUYLER P A, DUPONT W D, et al. The natural history of low-grade ductal carcinoma in situ of the breast in women treated by biopsy only revealed over 30 years of long-term follow-up[J]. Cancer, 2005, 103(12):2481-2484. |
[15] | YAMAMOTO Y, SAITO A, TATEISHI A, et al. Quantitative diagnosis of breast tumors by morphometric classifcation of microenvironmental myoepithelial cells using a machine learning approach[J]. Sci Rep, 2017,7:46732. |
[16] | FONDÓN I, SARMIENTO A, GARCÍA A I, et al. Automatic classifcation of tissue malignancy for breast carcinoma diagnosis[J]. Comput Biol Med, 2018,96:41-51. |
[17] | CRUZ-ROA A, GILMORE H, BASAVANHALLY A, et al. High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection[J]. PLoS One, 2018, 13(5):e0196828. |
[18] |
HAN Z, WEI B, ZHENG Y, et al. Breast cancer multi-classification from histopathological images with structured deep learning model[J]. Sci Rep, 2017, 7(1):4172.
doi: 10.1038/s41598-017-04075-z pmid: 28646155 |
[19] |
VETA M, VAN DIEST P J, WILLEMS S M, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images[J]. Med Image Anal, 2015, 20(1):237-248.
doi: 10.1016/j.media.2014.11.010 pmid: 25547073 |
[20] | WANG Y, ACS B, ROBERTSON S, et al. Improved breast cancer histological grading using deep learning[J]. Ann Oncol, 2022, 33(1):89-98. |
[21] | ROMO-BUCHELI D, JANOWCZYK A, GILMORE H, et al. Automated tubule nuclei quantification and correlation with Oncotype DX risk categories in ER+ breast cancer whole slide images[J]. Sci Rep, 2016,6:32706. |
[22] | KATAYAMA A, TOSS M S, PARKIN M, et al. Atypia in breast pathology: what pathologists need to know[J]. Pathology, 2022, 54(1):20-31. |
[23] | NATEGHI R, DANYALI H, HELFROUSH M S. A deep learning approach for mitosis detection: application in tumor proliferation prediction from whole slide images[J]. Artif Intell Med, 2021,114:102048. |
[24] |
ELSHARAWY K A, GERDS T A, RAKHA E A, et al. Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision[J]. Histopathology, 2021, 79(2):187-199.
doi: 10.1111/his.14354 pmid: 33590486 |
[25] | CHALLA B, TAHIR M, HU Y, et al. Artifcial intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workfow[J]. Mod Pathol, 2023, 36(8):100216. |
[26] |
STEINER D F, MACDONALD R, LIU Y, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer[J]. Am J Surg Pathol, 2018, 42(12):1636-1646.
doi: 10.1097/PAS.0000000000001151 pmid: 30312179 |
[27] |
HAMMOND M E, HAYES D F, DOWSETT M, et al. American society of clinical oncology/college of American pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer[J]. J Clin Oncol, 2010, 28(16):2784-2795.
doi: 10.1200/JCO.2009.25.6529 pmid: 20404251 |
[28] |
VIALE G, REGAN M M, MAIORANO E, et al. Prognostic and predictive value of centrally reviewed expression of estrogen and progesterone receptors in a randomized trial comparing letrozole and tamoxifen adjuvant therapy for postmenopausal early breast cancer: BIG 1-98[J]. J Clin Oncol, 2007, 25(25):3846-3852.
doi: 10.1200/JCO.2007.11.9453 pmid: 17679725 |
[29] |
AHERN T P, BECK A H, ROSNER B A, et al. Continuous measurement of breast tumour hormone receptor expression: a comparison of two computational pathology platforms[J]. J Clin Pathol, 2017, 70(5):428-434.
doi: 10.1136/jclinpath-2016-204107 pmid: 27729430 |
[30] | KOOPMAN T, BUIKEMA H J, HOLLEMA H, et al. What is the added value of digital image analysis of HER2 immunohistochemistry in breast cancer in clinical practice? A study with multiple platforms[J]. Histopatho-logy, 2019, 74(6):917-924. |
[31] | SERNA G, SIMONETTI S, FASANI R, et al. Sequential immunohistochemistry and virtual image reconstruction using a single slide for quantitative Ki67 measurement in breast cancer[J]. Breast, 2020,53:102-110. |
[32] | HUMPHRIES M P, HYNES S, BINGHAM V, et al. Automated tumour recognition and digital pathology scoring unravels new role for PD-L1 in predicting good outcome in ER-/HER2+ breast cancer[J]. J Oncol, 2018,2018,2937012. |
[33] | PILIPOW K, DARWICH A, LOSURDO A. T-cell-based breast cancer immunotherapy[J]. Semin Cancer Biol, 2021,72:90-101. |
[34] | SAVAS P, TEO Z L, LEFEVRE C, et al. The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”[J]. PLoS Med, 2016, 13(12):e1002204. |
[35] | 邢辉, 徐梓航, 董培, 等. 基于人工智能辅助乳腺癌新辅助治疗后肿瘤浸润淋巴细胞评估及可重复性分析[J]. 临床与实验病理学杂志, 2023, 39(7):776-781. |
XING F, XU Z H, DONG P, et al. Evaluation and reproducibility of artificial intelligence-assisted TILs after neoadjuvant therapy for breast cancer[J]. J Clin Exp Pathol, 2023, 39(7):776-781. | |
[36] |
RASMUSSON A, ZILENAITE D, NESTARENKAITE A, et al. Immunogradient indicators for antitumor response assessment by automated tumor-stroma interface zone detection[J]. Am J Pathol, 2020, 190(6):1309-1322.
doi: S0002-9440(20)30126-7 pmid: 32194048 |
[37] | 李凤玲, 卫亚妮, 步宏. 人工智能在新辅助治疗后乳腺癌疗效及预后预测中的研究现状[J]. 临床与实验病理学杂志, 2023, 39(7):833-837. |
LI F L, WEI Y N, BU H. Current research status of artificial intelligence in breast cancer efficacy and prognosis prediction after neoadjuvant therapy[J]. J Clin Exp Pathol, 2023, 39(7):833-837. | |
[38] | DODINGTON D W, LAGREE A, TABBARAH S, et al. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients[J]. Breast Cancer Res Treat, 2021, 186(2):379-389. |
[39] |
SAEDNIA K, LAGREE A, ALERA M A, et al. Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies[J]. Sci Rep, 2022, 12(1):9690.
doi: 10.1038/s41598-022-13917-4 pmid: 35690630 |
[40] |
LI F, YANG Y, WEI Y, et al. Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer[J]. NPJ Breast Cancer, 2022, 8(1):124.
doi: 10.1038/s41523-022-00491-1 pmid: 36418332 |
[41] | SAMMUT S J, CRISPIN-ORTUZAR M, CHIN S F, et al. Multi-omic machine learning predictor of breast cancer therapy response[J]. Nature, 2022, 601(7894):623-629. |
[42] | 邓仕杰, 刘蘅安, 杨春雪, 等. 数字化智慧病理科建设中数字病理扫描仪的选择[J]. 临床与实验病理学杂志, 2023, 39(7):847-851. |
DENG S J, LIU H A, YANG C X, et al. Selection of digital pathology scanners in the construction of digital smart pathology departments[J]. J Clin Exp Pathol, 2023, 39(7):847-851. | |
[43] | 卞修武, 张培培, 平轶芳, 等. 下一代诊断病理学[J]. 中华病理学杂志, 2022, 51(1):3-6. |
BIAN X W, ZHANG P P, PING Y F, et al. Next-generation diagnostic pathology[J]. Chin J Pathol, 2022, 51(1):3-6. | |
[1] | HAN B, ZHENG R, ZENG H, et al. Cancer incidence and mortality in China,2022[J]. J Natl Cancer Cent, 2024, 4(1):47-53. |
[2] |
CALDERARO J, KATHER J N. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers[J]. Gut, 2021, 70(6):1183-1193.
doi: 10.1136/gutjnl-2020-322880 pmid: 33214163 |
[3] | GIULIANO A E, CONNOLLY J L, EDGE S B, et al. Breast Cancer-Major changes in the American joint committee on cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(4):290-303. |
[4] | NIAZI M K K, PARWANI A V, GURCAN M N. Digital pathology and artificial intelligence[J]. Lancet Oncol, 2019, 20(5):e253-e261. |
[5] | YU K H, HEALEY E, LEONG T Y, et al. Medical artificial intelligence and human values[J]. N Engl J Med, 2024, 390(20):1895-1904. |
[6] | RAJKOMAR A, DEAN J, KOHANE I. Machine learning in medicine[J]. N Engl J Med, 2019, 380(14):1347-1358. |
[1] | XU Wangwang1,2 (徐旺旺), XU Liangfeng1,2 (许良凤), LIU Ninghui3(刘宁徽), LU Na3(律娜). Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 91-106. |
[2] |
HAO Kun, SUN Yuguang, WANG Rengui, et al.
Effect of debulking surgery on upper limb lymphedema after breast cancer surger [J]. Journal of Tissue Engineering and Reconstructive Surgery, 2024, 20(1): 69-. |
[3] | ZHANG Fengzhe, TONG Yiwei, CHEN Xiaosong, SHEN Kunwei. Analysis of risk factors for non-sentinel lymph node metastasis in patients with sentinel lymph node-negative breast cancer [J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 409-413. |
[4] | LU Yujie, ZHU Siji. The interpretation of Use of Adjuvant Bisphosphonates and Other Bone-Modifying Agents in Breast Cancer: ASCO-OH (CCO) Guideline Update [J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 405-408. |
[5] | HAN Mengyuan, CHEN Xiaosong. Hereditary breast cancer risk gene assessment and counseling: interpretation of NCCN guidelines and Ruijin Hospital clinical practice [J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 401-404. |
[6] | CAO Xi, LUO Yongchao, SHEN Songjie. Suitable breast cancer screening strategy for Chinese women [J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 382-388. |
[7] | ZHAO Xin, GAO Peng, CHEN Jie. Robotic-assisted surgical systems in treatment of breast cancer: applications and prospects [J]. Journal of Surgery Concepts & Practice, 2024, 29(05): 376-381. |
[8] | TANG Xiaolu, HUA Xin, CAO Lu, CHEN Jiayi. Application of 21-Gene test in adjuvant radiotherapy for early breast cancer [J]. Journal of Surgery Concepts & Practice, 2024, 29(03): 270-276. |
[9] | LIU Juan, YIN Lijuan, FAN Desheng. The clinicopathologic significance of AR, SKP2, SOX10, PD-L1 and TILs expression in triple-negative breast cancer [J]. Journal of Diagnostics Concepts & Practice, 2024, 23(02): 162-172. |
[10] | OU Dan, CAI Gang, CHEN Jiayi. Bioinformatics analysis for expression of RAD51AP1 in triple negative breast cancer with brain metastasis [J]. Journal of Diagnostics Concepts & Practice, 2024, 23(02): 146-154. |
[11] |
REN Yanxin, YU Yan, XU Kexin, et al.
Research progress of the effect of radiotherapy on breast reconstruction with prosthesis and autologous tissue after breast cancer surgery [J]. Journal of Tissue Engineering and Reconstructive Surgery, 2023, 19(5): 511-. |
[12] | LI Hui, YIN Yu, LI Chunxiao, et al. Research progress on rehabilitation effect of respiratory training on breast cancer-related lymphedema [J]. Journal of Tissue Engineering and Reconstructive Surgery, 2023, 19(4): 430-. |
[13] | ZHU Danli, BAO Wanting, WEI Hao, et al. Advances in breast reconstruction after breast cancer surgery [J]. Journal of Tissue Engineering and Reconstructive Surgery, 2023, 19(2): 201-. |
[14] | LI Yuefeng, HONG Jin, LI Zhian, RUAN Guodong, CHEN Weiguo. Prognostic analysis of the patients with HER2-positive breast cancer adjuvant treated with trastuzumab: a report of 1 246 cases [J]. Journal of Surgery Concepts & Practice, 2023, 28(05): 469-476. |
[15] | YANG Yi, YANG Xingxia, JIN Sili, ZHANG Xu, ZHU Juanying, CHEN Xiaosong. Clinical application of preoperative MRI examination in breast-conserving surgery for ductal carcinoma in situ [J]. Journal of Surgery Concepts & Practice, 2023, 28(04): 378-382. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||