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International Journal of Computer Assisted Radiology and Surgery

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01-04-2019 | Original Article

Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches

Authors: Jiao Li, Yan Song, Shuoyu Xu, Jinhua Wang, Huabin Huang, Weimei Ma, Xinhua Jiang, Yaopan Wu, Hongming Cai, Li Li

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2019

Abstract

Purpose

We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography.

Methods

Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods.

Results

Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade.

Conclusions

We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.

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Title: Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches
Authors: Jiao Li
Yan Song
Shuoyu Xu
Jinhua Wang
Huabin Huang
Weimei Ma
Xinhua Jiang
Yaopan Wu
Hongming Cai
Li Li
Publication date: 01-04-2019
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1900-x

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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