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Modern Pathology : An Official Journal... Jul 2021Microglandular adenosis (MGA)-related lesions, including atypical MGA (AMGA) and carcinoma involving MGA (C-MGA), are characterized by epithelial atypia, negative...
Microglandular adenosis (MGA)-related lesions, including atypical MGA (AMGA) and carcinoma involving MGA (C-MGA), are characterized by epithelial atypia, negative hormone receptors, and HER2 status, and can mimic invasive triple negative breast cancer (TNBC) in core needle biopsies (CNB) resulting in selection for treatment with neoadjuvant chemotherapy (NAC). We identified 12 cases of AMGA and/or C-MGA in post-NAC excision specimens (EXC) and analyzed their morphologic and immunohistochemical (IHC) features. All CNBs were initially diagnosed as containing TNBC. Upon re-review, TNBC was confirmed in nine cases. In three CNBs AMGA and/or C-MGA had been interpreted as TNBC. AMGA was initially recognized in only one case but AMGA and/or C-MGA were present in an additional nine CNBs. At EXC, no residual TNBC was present in 5 of 9 EXCs and all 12 cases showed residual AMGA and/or C-MGA. Similar to conventional MGA, AMGA, and C-MGA were positive for S-100, laminin and collagen IV and negative for calponin and p63. Following NAC, these lesions retained their typical staining pattern despite acquiring treatment-related morphologic alterations, most notably of which were areas of single cell growth pattern seen in eight EXCs. This study is the first to report the effects of NAC on AMGA and C-MGA. Our data showed no response of the AMGA and/or C-MGA following NAC in contrast to the high response rate of conventional TNBC. In particular, the infiltrative single cell pattern of post-NAC MGA-related lesions closely mimicked residual TNBC. The persistence of AMGA and C-MGA following NAC supports the notion that these lesions are distinct from conventional TNBC. Our findings also highlight the challenges in recognizing AMGA and C-MGA in CNBs which may lead to unwarranted treatment with NAC in the absence of conventional TNBC.
Topics: Adult; Aged; Breast Neoplasms; Carcinoma, Intraductal, Noninfiltrating; Chemotherapy, Adjuvant; Diagnosis, Differential; Female; Fibrocystic Breast Disease; Humans; Immunohistochemistry; Middle Aged; Neoadjuvant Therapy; Triple Negative Breast Neoplasms
PubMed: 33649459
DOI: 10.1038/s41379-021-00781-2 -
Frontiers in Oncology 2023To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.
OBJECTIVE
To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.
MATERIALS AND METHODS
A total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed.
RESULT
There was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets.
CONCLUSION
SA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.
PubMed: 37936612
DOI: 10.3389/fonc.2023.1276524 -
The Journal of Pathology Jun 2021Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed...
Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Topics: Aged; Aged, 80 and over; Artificial Intelligence; Biopsy; Biopsy, Large-Core Needle; Humans; Machine Learning; Male; Middle Aged; Pathologists; Prostate; Prostatic Neoplasms
PubMed: 33904171
DOI: 10.1002/path.5662 -
PloS One 2021Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women's health. With the help of computer vision...
Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women's health. With the help of computer vision technology, it has important application value to automatically classify pathological tissue images to assist doctors in rapid and accurate diagnosis. Breast pathological tissue images have complex and diverse characteristics, and the medical data set of breast pathological tissue images is small, which makes it difficult to automatically classify breast pathological tissues. In recent years, most of the researches have focused on the simple binary classification of benign and malignant, which cannot meet the actual needs for classification of pathological tissues. Therefore, based on deep convolutional neural network, model ensembleing, transfer learning, feature fusion technology, this paper designs an eight-class classification breast pathology diagnosis model BCDnet. A user inputs the patient's breast pathological tissue image, and the model can automatically determine what the disease is (Adenosis, Fibroadenoma, Tubular Adenoma, Phyllodes Tumor, Ductal Carcinoma, Lobular Carcinoma, Mucinous Carcinoma or Papillary Carcinoma). The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. After the information output by the fully connected layer of the two convolutional bases is fused, it is classified and output by the SoftMax function. The model experiment uses the publicly available BreaKHis data set. The number of samples of each class in the data set is extremely unevenly distributed. Compared with the binary classification, the number of samples in each class of the eight-class classification is also smaller. Therefore, the image segmentation method is used to expand the data set and the non-repeated random cropping method is used to balance the data set. Based on the balanced data set and the unbalanced data set, the BCDnet model, the pre-trained model Resnet50+ fine-tuning, and the pre-trained model VGG16+ fine-tuning are used for multiple comparison experiments. In the comparison experiment, the BCDnet model performed outstandingly, and the correct recognition rate of the eight-class classification model is higher than 98%. The results show that the model proposed in this paper and the method of improving the data set are reasonable and effective.
Topics: Biopsy; Breast; Breast Neoplasms; Datasets as Topic; Deep Learning; Female; Humans; Image Processing, Computer-Assisted
PubMed: 34252112
DOI: 10.1371/journal.pone.0253764 -
Archives of Pathology & Laboratory... Feb 2017-A review of amended pathology reports provides valuable information regarding defects in the surgical pathology process. (Review)
Review
CONTEXT
-A review of amended pathology reports provides valuable information regarding defects in the surgical pathology process.
OBJECTIVE
-To review amended breast pathology reports with emphasis placed on interpretative errors and their mechanisms of detection.
DESIGN
-All amended pathology reports for breast surgical specimens for a 5-year period at a large academic medical center were retrospectively identified and classified based on an established taxonomy.
RESULTS
-Of 12 228 breast pathology reports, 122 amended reports were identified. Most (88 cases; 72%) amendments were due to noninterpretative errors, including 58 report defects, 12 misidentifications, and 3 specimen defects. A few (34 cases; 27.9%) were classified as misinterpretations, including 14 major diagnostic changes (11.5% of all amendments). Among major changes, there were cases of missed microinvasion or small foci of invasion, missed micrometastasis, atypical ductal hyperplasia overcalled as ductal carcinoma in situ, ductal carcinoma in situ involving sclerosing adenosis mistaken for invasive carcinoma, lymphoma mistaken for invasive carcinoma, and amyloidosis misdiagnosed as fat necrosis. Nine major changes were detected at interpretation of receptor studies and were not associated with clinical consequences. Three cases were associated with clinical consequences, and of note, the same pathologist interpreted the corresponding receptor studies.
CONCLUSIONS
-Review of amended reports was a useful method for identifying error frequencies, types, and methods of detection. Any time that a case is revisited for ancillary studies or other reasons, it is an opportunity for the surgical pathologist to reconsider one's own or another's diagnosis.
Topics: Breast Neoplasms; Female; Humans; Medical Errors; Pathology, Surgical; Quality Assurance, Health Care; Retrospective Studies
PubMed: 27959581
DOI: 10.5858/arpa.2016-0018-OA -
Journal of Clinical Imaging Science 2023Microglandular adenosis (MGA) and atypical microglandular adenosis (AMGA) are intensely rare and distinctive forms of adenosis of the breast, usually occurring in...
Microglandular adenosis (MGA) and atypical microglandular adenosis (AMGA) are intensely rare and distinctive forms of adenosis of the breast, usually occurring in middle-aged women. Carcinoma arising in MGA is an extremely rare subtype of breast carcinoma, and most reported cases are of invasive carcinoma. Ultrasound and magnetic resonance imaging are accurate imaging modalities for diagnosing these abnormalities. Our goal in this article was to report a rare instance of ductal carcinoma (DCIS) arising from MGA and AMGA in a very young Vietnamese woman who presented with a palpable mass in her right breast for 1 month. During clinical examination and imaging, suspected lesions were found and categorized as BI-RADS 4a. The final histopathological findings confirmed DCIS arising from MGA/AMGA. In this patient, the disease was detected and managed early when the lesion was localized in the duct and there were no signs of invasive ductal carcinoma.
PubMed: 37292245
DOI: 10.25259/JCIS_32_2023 -
Cancers May 2022The gain-of-function mutation in the pleckstrin homology domain of AKT1 (AKT1E17K) occurs in lung and breast cancer. Through the use of human cellular models and of a...
The gain-of-function mutation in the pleckstrin homology domain of AKT1 (AKT1E17K) occurs in lung and breast cancer. Through the use of human cellular models and of a AKT1E17K transgenic Cre-inducible murine strain (R26-AKT1E17K mice), we have demonstrated that AKT1E17K is a bona fide oncogene for lung epithelial cells. However, the role of AKT1E17K in breast cancer remains to be determined. Here, we report the generation and the characterization of a MMTV-CRE; R26-AKT1E17K mouse strain that expresses the mutant AKT1E17K allele in the mammary epithelium. We observed that AKT1E17K stimulates the development of mammary tumors classified as ductal adenocarcinoma of medium-high grade and presented a variety of proliferative alterations classified as adenosis with low-to-high grade dysplasia in the mammary epithelium. A subsequent immunohistochemical characterization suggested they were PR/HER2/ER, basal-like and CK8/CK10/CK5/CK14. We also observed that, in parallel with an increased proliferation rate, tumors expressing mutant AKT1E17K presented an activation of the GSK3/cyclin D1 pathway in the mammary epithelium and cluster significantly with the human basal-like tumors. In conclusion, we demonstrate AKT1E17K is a bona fide oncogene that can initiate tumors at high efficiency in murine mammary epithelium in vivo.
PubMed: 35681625
DOI: 10.3390/cancers14112645 -
Frontiers in Oncology 2023We aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and...
OBJECTIVES
We 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.
METHODS
From 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.
RESULTS
Three 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 (<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.
CONCLUSIONS
The 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.
PubMed: 36959807
DOI: 10.3389/fonc.2023.1090617 -
Indian Journal of Pathology &... 2024Benign proliferative breast diseases are well recognized in young females. Benign biphasic proliferation of epithelial and myoepithelial cells has been observed, among...
Benign proliferative breast diseases are well recognized in young females. Benign biphasic proliferation of epithelial and myoepithelial cells has been observed, among which adeno-myoepithelial adenosis is one of the rare morphologies published in the literature with the tendency to recur and poses a risk for low-grade malignant transformation. Here, we report a case of a young female who had a history of recurrent breast lump mimicking phyllodes tumor and eventually diagnosed as adeno-myoepithelial adenosis on histopathological examination. Benign proliferative breast diseases are well recognized in young females. Benign biphasic proliferation of epithelial and myoepithelial cells has been observed, among which adeno-myoepithelial adenosis is one of the rare morphologies published in the literature with the tendency to recur and poses a risk for low-grade malignant transformation. Here, we report a case of a young female who had a history of recurrent breast lump mimicking phyllodes tumor and eventually diagnosed as adeno-myoepithelial adenosis on histopathological examination.
Topics: Female; Humans; Phyllodes Tumor; Neoplasm Recurrence, Local; Fibrocystic Breast Disease; Epithelial Cells; Hyperplasia; Cell Transformation, Neoplastic; Breast Neoplasms; Myoepithelioma
PubMed: 38358228
DOI: 10.4103/ijpm.ijpm_925_22 -
Diagnostic Pathology Dec 2022The multistep molecular model of breast carcinogenesis is based on the oestrogen receptor(ER) status of the tumour. Its two main arms comprise ER-positive and...
Proliferative epithelial changes in tumour adjacent tissue in Sri Lankan women with breast carcinoma: do morphological changes support molecular models of breast carcinogenesis?
BACKGROUND
The multistep molecular model of breast carcinogenesis is based on the oestrogen receptor(ER) status of the tumour. Its two main arms comprise ER-positive and ER-negative breast carcinomas(BCa), which are associated with Nottingham grade(NG) of the tumour and different proliferative epithelial changes. According to the model, columnar cell lesions(CCL), lobular carcinoma in-situ(LCIS) and atypical ductal hyperplasia(ADH), low-grade ductal carcinoma in-situ (LG-DCIS) are associated with low grade ER-positive tumours and microglandular adenosis (MGA), pleomorphic LCIS(PLCIS), high-grade DCIS(HG-DCIS) are associated with ER-negative high grade tumours. This study aims to describe the association between proliferative epithelial changes in breast tissue adjacent to tumour, in relation to the ER status and NG of the tumour.
METHODS
This descriptive cross-sectional study included 420, wide local excision and mastectomy specimens of BCa from National Hospital of Sri Lanka, between 2017-2019. The histopathological features of the tumour and proliferative epithelial changes in tumour adjacent tissue within 10 mm distance from the tumour-host interface were evaluated independently by two pathologists. The ER, PR(Progesterone receptor) and HER2 status assessed by immunohistochemistry(IHC) was reviewed. The associations between above epithelial lesions and ER status and NG{categorised as low grade (NG1 and NG2) and high grade (NG3)} of the tumour were analyzed.
RESULTS
ER positive BCa showed significant associations with CCH (p = 0.04), FEA (p = 0.035) and LGDCIS (p < 0.001). Although PLCIS was more frequent in ER positive tumours, the association did not attain statistical significance. ER negative BCa showed a significant association with HGDCIS (p = 0.016). CCLs as a whole (p = 0.005) and also CCC (p = 0.006) and FEA (p = 0.048) and LGDCIS (p < 0.001) showed significant associations with low NG tumours. High NG tumours showed a significant association with HGDCIS (p < 0.001). Microglandular adenosis was not identified in our study population.
CONCLUSION
These morphological findings support the multistep molecular based pathogenetic pathways of breast carcinoma in the studied setting in South Asia. Identification of these proliferative epithelial components in a core biopsy that is negative for BCa, should prompt for close clinicoradiological correlation, and if necessary re-biopsy of women suspected of harbouring a BCa.
Topics: Humans; Female; Breast Neoplasms; Carcinoma, Intraductal, Noninfiltrating; Sri Lanka; Cross-Sectional Studies; Mastectomy; Breast Carcinoma In Situ; Carcinogenesis; Carcinoma, Ductal, Breast
PubMed: 36581929
DOI: 10.1186/s13000-022-01281-w