Dataset Information

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.

ABSTRACT:

Background

Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast.

Methods

We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine.

Results

High diagnostic accuracy (81.8-99.3%) was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance.

Conclusions

In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.

SUBMITTER: Yamada M

PROVIDER: S-EPMC4763509 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.

Yamada Masatoshi M Saito Akira A Yamamoto Yoichiro Y Cosatto Eric E Kurata Atsushi A Nagao Toshitaka T Tateishi Ayako A Kuroda Masahiko M

Journal of pathology informatics 20160129

<h4>Background</h4>Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not ...[more]

PMID: 26955499

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Project description:ImportanceFollowing recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools.ObjectiveTo develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer.Design, setting, and participantsIn this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019.Main outcomes and measuresDiagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists.ResultsThe accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82).Conclusion and relevanceThe computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.

Project description:PurposeTo assess the accuracy of the shutter-speed approach compared with standard approach dynamic contrast material-enhanced magnetic resonance (MR) imaging pharmacokinetic analysis for breast cancer diagnosis.Materials and methodsThis study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained from 89 high-risk women (age range, 28-83 years) who had 92 suspicious lesions with negative findings at mammography (but visible at MR imaging). Each underwent a research dynamic contrast-enhanced MR imaging examination just prior to a clinical MR imaging-guided interventional procedure. Tumor region of interest (ROI) averaged and (for some) pixel-by-pixel dynamic contrast-enhanced time-course data, together with mean arterial input function, were subjected to serial standard and shutter-speed approach analyses to extract pharmacokinetic parameters, including rate constant for passive contrast reagent transfer between plasma and interstitium (K(trans)) and interstitial space volume fraction, or v(e). Pathologic findings were used as reference standards. Diagnostic accuracy was assessed with receiver operating characteristic analyses.ResultsThe pathologic analyses revealed 20 malignant and 72 benign lesions. Positive predictive value of the institutional clinical breast MR imaging protocol was 22%. At 100% sensitivity, ROI-averaged shutter-speed approach K(trans) had significantly (P = .008) higher diagnostic specificity than standard approach K(trans): 86.1% versus 77.8%. The difference in the ROI-averaged K(trans) parameter value, or ΔK(trans) (≡ K(trans) [shutter-speed approach] - K(trans) [standard approach]), had even higher specificity (88.9%). Combined use of ROI analysis and pixel-by-pixel mapping of ΔK(trans) achieved 98.6% specificity at 100% sensitivity.ConclusionThe use of the shutter-speed dynamic contrast-enhanced MR imaging method has the potential to improve breast cancer diagnostic accuracy and reduce putatively unnecessary biopsy procedures that yield benign pathologic findings.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11102413/-/DC1.

Dataset Information

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.

Background

Methods

Results

Conclusions

Publications

Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.

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