Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

by   Suzanne C Wetstein, et al.

Ductal 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 a deep learning-based DCIS grading system. It 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 observers (o1, o2 and o3) (κ_o1,dl=0.81, κ_o2,dl=0.53, κ_o3,dl=0.40) than the observers amongst each other (κ_o1,o2=0.58, κ_o1,o3=0.50, κ_o2,o3=0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κ_o1,dl=0.77, κ_o2,dl=0.75, κ_o3,dl=0.70) as the observers amongst each other (κ_o1,o2=0.77, κ_o1,o3=0.75, κ_o2,o3=0.72). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.



page 10

page 11

page 12


Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders

Objective and Impact Statement. We adopt a deep learning model for bone ...

Automated Gleason Grading of Prostate Biopsies using Deep Learning

The Gleason score is the most important prognostic marker for prostate c...

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs

Deep learning has demonstrated radiograph screening performances that ar...

BRACS: A Dataset for BReAst Carcinoma Subtyping in H E Histology Images

Breast cancer is the most commonly diagnosed cancer and registers the hi...

A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context

Prior skin image datasets have not addressed patient-level information o...

Spectral GUI for Automated Tissue and Lesion Segmentation of T1 Weighted Breast MR Images

We present Spectral GUI, a multiplatform breast MR image analysis tool d...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.