Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

2015 ◽  
Vol 24 (11) ◽  
pp. 4041-4054 ◽  
Author(s):  
Angshuman Paul ◽  
Dipti Prasad Mukherjee
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Mitosis Detection ◽  
Histopathological Images ◽  
Cancer Grading

scholarly journals Variation in breast cancer grading in 1,636 resections assessed using control charts and in silico kappa

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242656
Author(s):  
Jinesa Moodley ◽  
Phillip Williams ◽  
Gabriela Gohla ◽  
Pierre Major ◽  
Michael Bonert
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
In Silico ◽  
Control Charts ◽  
Next Generation ◽  
Nottingham Grade ◽  
Exclusive Category ◽  
Insight Into ◽  
Cancer Grading

Objective Assess interpretative variation in Nottingham grading using control charts (CCs) and in silico kappa (ISK). Methods In house invasive breast cancer cases (2011–2019) at two institutions with a synoptic report were extracted. Pathologist interpretative rates (PIRs) were calculated and normed for Nottingham grade (G) and its components (tubular score (TS), nuclear score (NS), mitotic score (MS)) for pathologists interpreting >35 cases. ISKs were calculated using the ordered mutually exclusive category assumption (OMECA) and maximal categorical overlap assumption (MCOA). Results The study period included 1,994 resections. Ten pathologists each assessed 38–441 cases and together saw 1,636; these were further analyzed. The PIR medians (normed ranges) were: G1:24%(18–27%), G2:53%(43–56%) and G3:26%(19–33%). The MCOA ISK and the number of statistical outliers (p< 0.05/p< 0.001) to the group median interpretive rate (GMIR) for the ten pathologists was G1: 0.82(2/0 of 10), G2: 0.76(1/1), G3: 0.71(3/1), TS1: 0.79(1/0), TS2: 0.63(5/1), TS3: 0.66(5/1), NS1: 0.37(5/4), NS2: 0.60(4/3), NS3: 0.59(4/4), MS1: 0.78(3/1), MS2: 0.78(3/1), MS3: 0.77(2/0). The OMECA ISK was 0.62, 0.49, 0.69 and 0.71 for TS, NS, MS and G. Conclusions The nuclear score has the most outliers. NS1 appears to be inconsistently used. ISK mirrors trends in conventional kappa studies. CCs and ISK allow insight into interpretive variation and may be essential for the next generation in quality.

scholarly journals A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anabia Sohail ◽  
Asifullah Khan ◽  
Noorul Wahab ◽  
Aneela Zameer ◽  
Saranjam Khan
Keyword(s):  
Breast Cancer ◽  
Activity Index ◽  
Tissue Level ◽  
Cell Level ◽  
Discrimination Ability ◽  
Mitosis Detection ◽  
Histopathological Images ◽  
Deep Cnn ◽  
Domain Transfer ◽  
Multi Phase

AbstractThe mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

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