Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens

D’Alfonso, Timothy M.; Ho, David Joon; Hanna, Matthew G.; Grabenstetter, Anne; Yarlagadda, Dig Vijay Kumar; Geneslaw, Luke; Ntiamoah, Peter; Fuchs, Thomas J.; Tan, Lee K.

doi:10.1038/s41379-021-00807-9

Cited by 19 publications

(7 citation statements)

References 22 publications

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“…It is important to balance the data for binary classification since classification metrics generate reliable results only when applied to balanced datasets and produce misleading results when applied to imbalanced cases (Chicco and Jurman, 2020). However, one of the main limitations of this study is the use of only the four MFs (40×, 100×, 200× and 400×) proposed by the BreakHis dataset; there are other MFs used by pathologists for BC histopathological image diagnosis (5×, 10× and 20×) (Ho et al , 2021; D'Alfonso et al , 2021). Thus, it would be interesting to study whether the robustness of the models developed hold for different BC image datasets with different MFs.…”

Section: Threats Of Validitymentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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PurposeHistopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.Design/methodology/approachThree pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.FindingsThis study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.Originality/valueThe best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

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Section: Threats Of Validitymentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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“…The current gold standard for margin determination is based on histopathological microscopic imaging of the tissue sections stained with H&E. A variety of optical imaging techniques have been demonstrated to enhance breast tumor margin detection in H&E slides of post-lumpectomy samples. 3 Although the techniques are capable of precise determination of tumor margins, detectable tissue types are limited due to the staining targets nuclei, connective tissue, and fat only. Also, the H&E reading requires the trained eyes of the histopathologists.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery

Hwang,

Cheney,

Kanick

et al. 2024

J. Biomed. Opt.

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. Significance Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the -means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised -means algorithm. The unsupervised -means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas’ unique endmembers produced by the two methods agree with each other within residual error margin. Conclusions Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas’ unique endmembers used by the two methods agree to residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

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“… 11 Similarly, another deep learning model was used as a screening tool for breast lumpectomy shaved margin assessment to save time for pathologists by excluding the majority of benign tissue samples. 12 In addition, deep learning models have been investigated to discover novel morphological patterns indicating molecular subtypes from histologic images. 13 Correlating digitized pathologic images with molecular information has contributed to prognosis prediction and personalized medicine.…”

Section: Introductionmentioning

confidence: 99%

Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

Chui

Vanderbilt

et al. 2023

Journal of Pathology Informatics

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Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens

Cited by 19 publications

References 22 publications

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery

Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

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