Cytological diagnosis of sclerosing adenosis of breast: Diagnostic challenges and literature review

Sharma, Tanya; Chaurasia, Jai Kumar; Kumar, Vikas; Mukhopadhyay, Sabyasachi; Joshi, Deepti

doi:10.1111/cyt.13041

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The performance of this model needs to be verified by other centers and a larger cohort in the future. (3) We only included patients with SA and IDC, though the differences in the texture features for the pathological subtypes of breast cancer and adenosis can be analyzed in the future.…”

Section: Discussionmentioning

confidence: 99%

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An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

Huang

Nong²,

Tang³

et al. 2023

Front. Oncol.

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ObjectivesWe aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies.MethodsFrom January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model.ResultsThree predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p<0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness.ConclusionsThe model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.

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Section: Discussionmentioning

confidence: 99%

“…Sclerosing adenosis (SA) is a common benign lesion that may mimic breast malignancy clinically, radiologically, and pathologically (1)(2)(3)(4). SA is usually asymptomatic or palpated with a mass, which is unexpectedly found in premenopausal women who have been examined using imaging or histopathology for other reasons (2).…”

Section: Introductionmentioning

confidence: 99%

An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

Huang

Nong²,

Tang³

et al. 2023

Front. Oncol.

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“…Radiologically, some instances of breast adenosis may be erroneously interpreted as invasive ductal carcinoma (IDC). While breast adenosis typically only requires observation and monitoring, IDC necessitates a comprehensive treatment plan involving surgery, radiation, and chemotherapy ( 4 , 5 ). Therefore, accurate preoperative differentiation between breast adenosis and IDC is imperative for precise diagnosis and optimal patient management.…”

Section: Introductionmentioning

confidence: 99%

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Li,

Song,

Qian

et al. 2024

Front. Oncol.

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ObjectivesTo explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).MethodsData from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.ResultsIn the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05).ConclusionThe effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

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Hormone therapy in women with benign breast disease – What little is known and suggestions for clinical implementation

Sahni,

Fraker,

Cornell

et al. 2024

Maturitas

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Cytological diagnosis of sclerosing adenosis of breast: Diagnostic challenges and literature review

Abstract: This report describes the cytological diagnosis of sclerosing adenosis along with a brief literature review, with emphasis on overcoming diagnostic challenges and pitfalls.

Cited by 3 publications

References 15 publications

An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Hormone therapy in women with benign breast disease – What little is known and suggestions for clinical implementation

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