IMPROVED ACCURACY RATES OF FNAC INTERPRETATION IN BENIGN BREAST LESIONS.
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ABSTRACT:
SUBMITTER: Deshpande GU
PROVIDER: S-EPMC5531778 | biostudies-other | 1999 Jan
REPOSITORIES: biostudies-other
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Project description:Importance:Use of digital whole-slide imaging (WSI) for dermatopathology in general has been noted to be similar to traditional microscopy (TM); however, concern has been noted that WSI is inferior for interpretation of melanocytic lesions. Since approximately 1 of every 4 skin biopsies is of a melanocytic lesion, the use of WSI requires verification before use in clinical practice. Objective:To compare pathologists' accuracy and reproducibility in diagnosing melanocytic lesions using Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) categories when analyzing by TM vs WSI. Design, Setting, and Participants:A total of 87 pathologists in community-based and academic settings from 10 US states were randomized with stratification based on clinical experience to interpret in TM format 180 skin biopsy cases of melanocytic lesions, including 90 invasive melanoma, divided into 5 sets of 36 cases (phase 1). The pathologists were then randomized via stratified permuted block randomization with block size 2 to interpret cases in either TM (n?=?46) or WSI format (n?=?41), with each pathologist interpreting the same 36 cases on 2 separate occasions (phase 2). Diagnoses were categorized as MPATH-Dx categories I through V, with I indicating the least severe and V the most severe. Main Outcomes and Measures:Accuracy with respect to a consensus reference diagnosis and the reproducibility of repeated interpretations of the same cases. Results:Of the 87 pathologists in the study, 46% (40) were women and the mean (SD) age was 50.7 (10.2) years. Except for class III melanocytic lesions, the diagnostic categories showed no significant differences in diagnostic accuracy between TM and WSI interpretation. Discordance was lower among class III lesions for the TM interpretation arm (51%; 95% CI, 46%-57%) than for the WSI arm (61%; 95% CI, 53%-69%) (P?=?.05). This difference is likely to have clinical significance, because 6% of TM vs 11% of WSI class III lesions were interpreted as invasive melanoma. Reproducibility was similar between the traditional and digital formats overall (66.4%; 95% CI, 63.3%-69.3%; and 62.7%; 95% CI, 59.5%-65.7%, respectively), and for all classes, although class III showed a nonsignificant lower intraobserver agreement for digital. Significantly more mitotic figures were detected with TM compared with WSI: mean (SD) TM, 6.72 (2.89); WSI, 5.84 (2.56); P?=?.002. Conclusions and Relevance:Interpretive accuracy for melanocytic lesions was similar for WSI and TM slides except for class III lesions. We found no clinically meaningful differences in reproducibility for any of the diagnostic classes.
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Project description:ObjectivesThe aim of this study was to evaluate breast multiparametric ultrasound (mpUS) and its potential to reduce unnecessary breast biopsies with 1, 2, or 3 additional quantitative parameters (Doppler, elastography, and contrast-enhanced ultrasound [CEUS]) to B-mode and investigate possible variations with different reader experience.Materials and methodsThis prospective study included 124 women (age range, 18-82 years; mean, 52 years), each with 1 new breast lesion, scheduled for ultrasound-guided biopsy between October 2015 and September 2016. Each lesion was examined with B-mode, elastography (Virtual Touch IQ [VTIQ]), Doppler, and CEUS, and different quantitative parameters were recorded for each modality. Four readers (2 experienced breast radiologists and 2 in-training) independently evaluated B-mode images of each lesion and assigned a BI-RADS (Breast Imaging Reporting and Data System) score. Using the area under the receiver operating characteristic curve (AUC), the most accurate quantitative parameter for each modality was chosen. These were then combined with the BI-RADS scores of all readers. Descriptive statistics and AUC were used to evaluate the diagnostic performance of mpUS.ResultsSixty-five lesions were malignant. MpUS with B-mode and 2 additional quantitative parameters (VTIQ and CEUS or Doppler) showed the highest diagnostic performance for all readers (averaged AUCs, 0.812-0.789 respectively vs 0.683 for B-mode, P = 0.0001). Both combinations significantly reduced the number of false-positive findings up to 46.9% (P < 0.0001).ConclusionsQuantitative mpUS with 2 different triple assessment modalities (B-mode, VTIQ elastography, CEUS, or Doppler) shows the best diagnostic performance for breast cancer diagnosis and leads to a significant reduction of false-positive biopsy recommendations, for both experienced and inexperienced readers.
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Project description:BACKGROUND:Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls. METHODS:Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions. RESULTS:Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification. CONCLUSION:A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.
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Project description:BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.
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