scholarly journals Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound

2021 ◽  
Vol 9 (4) ◽  
pp. 295-295
Author(s):  
Lang Qian ◽  
Zhikun Lv ◽  
Kai Zhang ◽  
Kun Wang ◽  
Qian Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma

scholarly journals A deep learning model for breast ductal carcinoma in situ classification in whole slide images

2022 ◽  
Author(s):  
Fahdi Kanavati ◽  
Shin Ichihara ◽  
Masayuki Tsuneki
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
New Techniques ◽  
Large Numbers ◽  
Computational Pathology ◽  
Deep Learning Model ◽  
Whole Slide Images

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n=1,382, n=548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.

scholarly journals Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

2021 ◽  
Vol 101 (4) ◽  
pp. 525-533
Author(s):  
Suzanne C. Wetstein ◽  
Nikolas Stathonikos ◽  
Josien P. W. Pluim ◽  
Yujing J. Heng ◽  
Natalie D. ter Hoeve ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Learning System ◽  
Observer Agreement ◽  
Grading System ◽  
Patient Level ◽  
Lesion Level

AbstractDuctal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen’s kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

scholarly journals Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features

2018 ◽  
Vol 15 (3) ◽  
pp. 527-534 ◽  
Author(s):  
Bibo Shi ◽  
Lars J. Grimm ◽  
Maciej A. Mazurowski ◽  
Jay A. Baker ◽  
Jeffrey R. Marks ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Invasive Disease ◽  
Deep Learning Features

scholarly journals Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

2019 ◽  
Vol 115 ◽  
pp. 103498 ◽  
Author(s):  
Zhe Zhu ◽  
Michael Harowicz ◽  
Jun Zhang ◽  
Ashirbani Saha ◽  
Lars J. Grimm ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Invasive Disease ◽  
Learning Analysis

scholarly journals Unmasking the immune microecology of ductal carcinoma in situ with deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Priya Lakshmi Narayanan ◽  
Shan E. Ahmed Raza ◽  
Allison H. Hall ◽  
Jeffrey R. Marks ◽  
Lorraine King ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Digital Pathology ◽  
Deep Convolutional Neural Networks ◽  
Technological Developments ◽  
Infiltrating Lymphocytes ◽  
Spatial Architecture

AbstractDespite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2–3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.

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