Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001621 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850119373871644672 |
|---|---|
| author | Ricardo Gonzalez Peyman Nejat Ashirbani Saha Clinton J.V. Campbell Andrew P. Norgan Cynthia Lokker |
| author_facet | Ricardo Gonzalez Peyman Nejat Ashirbani Saha Clinton J.V. Campbell Andrew P. Norgan Cynthia Lokker |
| author_sort | Ricardo Gonzalez |
| collection | DOAJ |
| description | Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2–2.6) and 1.8 (95% CI, 1.3–2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11–3.29) for recurrence, and between 0.09 (95% CI, 0.01–0.70) and 0.65 (95% CI, 0.43–0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses. |
| format | Article |
| id | doaj-art-ed85b544a79a424bb2034422e6467e53 |
| institution | OA Journals |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-ed85b544a79a424bb2034422e6467e532025-08-20T02:35:39ZengElsevierJournal of Pathology Informatics2153-35392024-12-011510034810.1016/j.jpi.2023.100348Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic reviewRicardo Gonzalez0Peyman Nejat1Ashirbani Saha2Clinton J.V. Campbell3Andrew P. Norgan4Cynthia Lokker5DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada; Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, CanadaWilliam Osler Health System, Brampton, Ontario, Canada; Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, CanadaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesHealth Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, CanadaNumerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2–2.6) and 1.8 (95% CI, 1.3–2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11–3.29) for recurrence, and between 0.09 (95% CI, 0.01–0.70) and 0.65 (95% CI, 0.43–0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.http://www.sciencedirect.com/science/article/pii/S2153353923001621Breast neoplasmsPathologyValidation studiesMachine learningSystematic review |
| spellingShingle | Ricardo Gonzalez Peyman Nejat Ashirbani Saha Clinton J.V. Campbell Andrew P. Norgan Cynthia Lokker Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review Journal of Pathology Informatics Breast neoplasms Pathology Validation studies Machine learning Systematic review |
| title | Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review |
| title_full | Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review |
| title_fullStr | Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review |
| title_full_unstemmed | Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review |
| title_short | Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review |
| title_sort | performance of externally validated machine learning models based on histopathology images for the diagnosis classification prognosis or treatment outcome prediction in female breast cancer a systematic review |
| topic | Breast neoplasms Pathology Validation studies Machine learning Systematic review |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001621 |
| work_keys_str_mv | AT ricardogonzalez performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview AT peymannejat performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview AT ashirbanisaha performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview AT clintonjvcampbell performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview AT andrewpnorgan performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview AT cynthialokker performanceofexternallyvalidatedmachinelearningmodelsbasedonhistopathologyimagesforthediagnosisclassificationprognosisortreatmentoutcomepredictioninfemalebreastcancerasystematicreview |