Granular cell tumor of the breast: a multidisciplinary challenge

Meani, Francesco; Lascio, Simona Di; Wandschneider, Wiebke; Montagna, Giacomo; Vitale, Valerio; Zehbe, Sabine; Harder, Yves; Parvex, Sandra Leoni; Spina, Paolo; Canonica, Claudia; Generali, Daniele; Pagani, Olivia

doi:10.1016/j.critrevonc.2019.102828

Cited by 24 publications

(36 citation statements)

References 33 publications

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“…Two were invasive ductal carcinomas, not needing comments. The other was instead a rare entity (a granular cell tumor, usually considered benign but deserving surgery [16]) that can be considered an "expected" false positive case.…”

Section: Discussionmentioning

confidence: 99%

“…These masses were invasive ductal carcinomas according to histopathology, thus the radiologist assigned a more appropriate class of malignancy. The last mass was a granular cell tumor at histopathology, usually considered benign, to which mass both the model and the radiologist assigned a wrong malignancy BI-RADS class (BI-RADS 4 and 5, respectively); however we must consider that from a therapeutical point of view this type of tumor (a rare entity derived from Schwann cells) is aggressive and locally recurrent, therefore requiring surgical excision with curative intent [16].…”

Section: Bi-rads Diagnostic Categories Classificationmentioning

confidence: 99%

“…preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted December 17, 2021. ; https://doi.org/10.1101/2021.12. 16.21267907 doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

Interlenghi¹,

Salvatore

Magni

et al. 2021

Preprint

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We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision making towards short-interval follow-up versus tissue sampling. From a retrospective 2015–2019 series of ultrasound-guided core needle biopsies performed by four board-cer-tified breast radiologists using six ultrasound systems from three vendors, we collected 821 images of 834 suspicious breast masses from 819 patients, 404 malignant and 430 benign according to histopathology. A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions were used for training and cross-validation of ensembles of ma-chine learning algorithms supervised during learning by histopathological diagnosis as a reference standard. Based on a majority vote (over 80% of the votes to have a valid prediction of benign lesion), an ensemble of support vector machines showed an ability to reduce the biopsy rate of benign lesions by 15% to 18%, always keeping a sensitivity over 94%, when externally tested on 236 images from two image sets: 1) 123 lesions (51 malignant and 72 benign) obtained from the same four ultrasound systems used for training, resulting into a positive predictive value (PPV) of 45.9% (95% confidence inter-val 36.3-55.7%) versus a radiologists’ PPV of 41.5% (p < 0.005), combined with a 98.0% sensitivity (89.6–99.9%); 2) 113 lesions (54 malignant and 59 benign) obtained from two ultrasound systems from vendors different from those used for training, resulting into a 50.5% PPV (40.4–60.6%) versus a radiologists’ PPV of 47.8% (p < 0.005), combined with a 94.4% sensitivity (84.6–98.8%). Errors in BI-RADS 3 category (i.e., assigned by the model as BI-RADS 4) were 0.8% and 2.7% in the Testing set I and II, respectively. The board-certified breast radiologist accepted the BI-RADS classes assigned by the model in 114 masses (92.7%) and modified the BI-RADS classes of 9 breast masses (7.3%). In 6 of 9 cases the model performed better than the radiologist, since it assigned a BI-RADS 3 classification to histopathology-confirmed benign masses that were classified as BI-RADS 4 by the radiologist.

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Section: Discussionmentioning

confidence: 99%

Section: Bi-rads Diagnostic Categories Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

Interlenghi¹,

Salvatore

Magni

et al. 2021

Preprint

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“…While the prognosis of a GrCT is generally good, metastatic disease has been described. The recommended treatment is wide surgical excision considering the higher risk of recurrence with positive resection margins [1].…”

Section: Competing Interestsmentioning

confidence: 99%

Granular Cell Tumor: A Mimicker of Breast Carcinoma

Bosmans

Dekeyzer

Vanhoenacker³

2021

Journal of the Belgian Society of Radiology

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“…Granular cell tumor is seen in the breast in 5-6% of cases [4,6]. GCT of the breast is generally benign; it rarely shows malignant features [7].…”

Section: Introductionmentioning

confidence: 99%

Granular cell tumor presenting as an axillary mass: A case report

Çıtgez

Yiğit

Yetkin

et al. 2020

Archives of Clinical and Experimental Medicine

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Granular cell tumor (GCT) is a tumor that can arise at virtually any body site. About 5 to 15% of the cases occur in the breast, and it is relatively rare in the axilla. %1-2 of granular cell tumors are malignant. The clinical symptoms and imaging examinations of breast GCTs are non-specific and can easily be confused with malignant tumors. Most are benign and reportedly malignant cases are rare. We report a 59-year-old male with granular cell tumor in axillary region existed approximately 2 years. The palpable mass was around 15mm in diameter and felt clinically suspicious of malignancy. Tru-cut biopsy of the mass showed granular cell tumor features and surgical excision was performed. Early diagnosis and complete resection of the tumor remains the best treatment method.

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Granular cell tumor of the breast: a multidisciplinary challenge

Cited by 24 publications

References 33 publications

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

Granular Cell Tumor: A Mimicker of Breast Carcinoma

Granular cell tumor presenting as an axillary mass: A case report

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