Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Baker, Jay A.; Marks, Jeffrey R.; King, Lorraine; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

doi:10.1016/j.acra.2017.03.013

Cited by 21 publications

(7 citation statements)

References 37 publications

(38 reference statements)

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“…Each breast lesion was identified by a fellowship-trained breast radiologist (L.J.G., with 6 years of experience), who was provided all available images and reports to guide his annotations. Calcifications were automatically segmented by using a U-Net convolutional neural network trained on a previously reported computer vision algorithm (14,15).…”

Section: Feature Extractionmentioning

confidence: 99%

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features

et al. 2022

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The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Background: Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery.Purpose: To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods:In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or x 2 test. Results:The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years 6 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years 6 10; test set, 59 years 6 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion:Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning.

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Section: Feature Extractionmentioning

confidence: 99%

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features

et al. 2022

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“…(7) Most interestingly though was the agreement that both supplemental US and MRI should be used to exclude occult invasive disease in active surveillance eligible patients. The safe use of active surveillance depends in large part on the ability to identify patients with pure DCIS, but previous efforts to predict occult invasive disease on mammography have shown limited success (26)(27)(28). Respondents may feel that the added information provided by multi-modality imaging will give them more confidence in excluding invasive disease.…”

Section: Discussionmentioning

confidence: 99%

“…Strongly agree 117 (25) Agree 120 (25) Neutral 123 (26) Disagree 68 (14) Strongly disagree 49 (10) It is important to use supplemental MRI to exclude occult invasive disease in women considering active surveillance for DCIS.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Ductal Carcinoma In Situ Biology, Language, and Active Surveillance: A Survey of Breast Radiologists’ Knowledge and Opinions

Grimm

Destounis

Rahbar

et al. 2020

Journal of the American College of Radiology

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Purpose: To understand how breast radiologists perceive ductal carcinoma in situ (DCIS). Materials and methods:A 19-item survey was developed by the Society of Breast Imaging Patient Care and Delivery Committee and distributed to all SBI members. The survey queried respondents' demographics, knowledge of DCIS biology, language used to discuss a new diagnosis of DCIS, and perspectives on active surveillance for DCIS. Five-point Likert scales (1=strongly disagree, 3=neutral, 5=strongly agree) were used.Results: There were 536 responses for a response rate of 41%. There was agreement that DCIS is the primary driver of overdiagnosis in breast cancer screening (median 4) and respondents provided mean and median overdiagnosis estimates of 29.7% and 25% for low-grade DCIS as well as 4.2% and 0% for high-grade DCIS respectively. Respondents were varied in how they describe DCIS but most often use the word 'cancer' with a qualifier such as early (32%) or pre-invasive (25%). Respondents disagreed (median 2) with removing the word 'carcinoma' from DCIS. Finally, there was agreement that current standard of care therapy for some forms of DCIS is overtreatment (median 4) and that active surveillance as an alternative management strategy should be studied (mean 4), but felt that US (median 4) and MRI (median 4) should be used to exclude women with occult invasive disease prior to active surveillance.

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“…More recently, a quantitative imaging analysis method, called radiomics, has emerged as a promising tool for extracting a large number of quantitative features from medical imaging data ( 15 ). While a few studies have tried to utilize the radiomics features for the prediction of upgrading of DCIS in limited efforts ( 16 , 17 ), the extraction of large-scale radiomics features from imaging data and the training of a machine learning classifier using these features will add knowledge to the prior findings and help to evaluate the potential of this method for noninvasive classification between pure and upgraded DCIS.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging

et al. 2022

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ObjectiveTo investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.Materials and methodsThis retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set.ResultsThe model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619–0.829) and 0.742 (0.623–0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575–0.892) and 0.7 (0.558–0.842), 0.714 (0.608–0.820) and 0.767 (0.651–0.882), respectively.ConclusionOur study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

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Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Cited by 21 publications

References 37 publications

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features

Ductal Carcinoma In Situ Biology, Language, and Active Surveillance: A Survey of Breast Radiologists’ Knowledge and Opinions

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging

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