Primary breast mucinous cystadenocarcinoma-histopathological analysis.
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ABSTRACT:
SUBMITTER: Budzik MP
PROVIDER: S-EPMC10007875 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
Publications
Primary breast mucinous cystadenocarcinoma-histopathological analysis.
Translational cancer research 20230109 2
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Project description:Mucinous cystadenocarcinoma (MCA) is a rare breast cancer. The present study reports a case of primary MCA of the breast with a comprehensive evaluation of this rare tumour. A 51-year-old woman sought medical attention for a mass in the left breast. A core needle biopsy revealed an infiltrating adenocarcinoma with mucus secretion and papillary formation. The macroscopic appearance was of a greyish-white, tough and well-circumscribed solid mass, without a notable cyst. Microscopically, the tumour consisted of ducts and cysts of varying sizes. Varying degrees of branching papillary structures were observed in the lumen and cyst cavities. The tumour cells were highly columnar in shape, with high-grade nuclei arranged in a single-layer. Immunohistochemistry revealed that the tumour was a basal-like triple-negative breast cancer with a high proliferation index and tumour protein p53 diffuse strong expression. Mutations in breast cancer 1-associated RING domain 1 (BARD1), kinase domain containing receptor (KDR), mucin-6 (MUC6), tumour protein 53 (TP53) and breast cancer 1-interacting protein C-terminal helicase 1 (BRIP1) were identified using DNA analysis. The patient was followed up for 26 months and showed no signs of recurrence or metastasis. In conclusion, the current study presents a case of MCA of breast accompanied by mutations in the BARD1, KDR, MUC6, TP53 and BRIP1 genes, with no recurrence after a 26-month follow-up. Combining this case with a review of the literature helps us to better understand the clinicopathological and genetic characteristics of MCA, and guide treatment.
| S-EPMC11602826 | biostudies-literature
Project description:BackgroundMucinous cystadenocarcinoma (MCA) mainly occurs in the ovary, pancreas, and appendix, whereas the breast is a rare primary site of occurrence. Invasive ductal carcinoma (IDC) is the most common breast malignancy. Only 31 cases of the breast MCA have been reported in the English literature, and the coexistence of MCA and IDC in the breast are rare.Case descriptionHere, we describe a 61-year-old postmenopausal woman with no family history of breast cancer or other breast-related diseases, who presented with a palpable mass in her left breast lasting for 2 months. On ultrasonography examination, the tumor was a cystic-solid lesion with clear boundary. Magnetic resonance imaging (MRI) showed a mass with low signal intensity on T1 weighted imaging and high signal intensity on T2 weighted imaging. Intraoperative frozen sections revealed metastatic tumor cells in one sentinel lymph node (1/4). She then underwent left modified radical mastectomy with axillary dissection. The post-operative pathological examination showed the tumor consisted mostly of MCA (60%), with a small proportion of intermediate-grade IDC. The MCA had a well-demarcated cystic structure with papillary projections and abundant mucoid material. The epithelium lining cystic spaces was tall columnar, with mucin-producing cells that had basally located nuclei. The degree of cytological atypia varied considerably. Axillary lymph nodes were normal (0/15). The MCA was triple-negative for estrogen receptor (ER), progesterone receptor (PR), and HER2, and positive for CK7 but negative for CK20. Through next-generation sequencing, no mutations in the BRCA1 and BRCA2 genes were identified in our case, which was not highlighted in prior cases. After surgery, the patient underwent eight cycles of chemotherapy, and she has been disease-free during the 10-month follow-up. In addition to detailing this instance of mixed MCA and IDC of the breast, we reviewed relevant literature and compare our findings with other patients who had breast MCAs.ConclusionsOur results improved the understanding of mixed MCA and IDC, especially MCA, and provided a basis for its diagnosis and differential diagnosis from other metastatic diseases.
| S-EPMC9834603 | biostudies-literature
Project description:BackgroundMucinous cystadenocarcinoma (MC) of the kidney is a rare renal epithelial tumor originating from the renal pelvic urothelium. There are only a few published reports on MC. Due to its rare and unknown tissue origin, its diagnosis is difficult which almost can be diagnosed through the pathological method.Case presentationIn this case report, we report a female patient whose chief complaint was low back pain lasting for one month. The three-dimensional computed tomography scan of the urinary system detected approximately 7 cm of a left renal cystic mass. The renal cystic mass was diagnosed as MC after robot-assisted laparoscopic radical nephrectomy. The MC originated from the kidney after completing colorectal adenocarcinoma and ovarian adenocarcinoma.ConclusionsWe reported a case of MC of the kidney which was a rare renal tumor. We not only aimed to present an unusual case of MC and review the previous literature on its pathology and differential diagnosis, but also used new method to treat this type of tumor.
| S-EPMC9852041 | biostudies-literature
Project description:BackgroundData on the clinicopathological characteristics of mucinous gastric cancer (muc-GC) are limited. This study compares the clinical outcome and response to chemotherapy between patients with resectable muc-GC, intestinal (int-GC), and diffuse (dif-GC) gastric cancer.MethodsPatients from the D1/D2 study or the CRITICS trial were included in exploratory surgery-alone (SAtest) or chemotherapy test (CTtest) cohorts. Real-world data from the Netherlands Cancer Registry on patients treated between with surgery alone (SAvalidation) and receiving preoperative chemotherapy with or without postoperative treatment (CTvalidation) were used for validation. Histopathological subtypes were extracted from pathology reports filed in the Dutch Pathology Registry and correlated with tumor regression grade (TRG) and relative survival (RS).ResultsIn the SAtest (n = 549) and SAvalidation (n = 8062) cohorts, muc-GC patients had a 5-year RS of 39% and 31%, similar to or slightly better than dif-GC (43% and 29%, P = .52 and P = .011), but worse than int-GC (55% and 42%, P = .11 and P < .001). In the CTtest (n = 651) and CTvalidation (n = 2889) cohorts, muc-GC showed favorable TRG (38% and 44% (near-) complete response) compared with int-GC (26% and 35%) and dif-GC (10% and 28%, P < .001 and P = .005). The 5-year RS in the CTtest and CTvalidation cohorts for muc-GC (53% and 48%) and int-GC (58% and 59%) was significantly better compared with dif-GC (35% and 38%, P = .004 and P < .001).ConclusionRecognizing and incorporating muc-GC into treatment decision-making of resectable GC can lead to more personalized and effective approaches, given its favorable response to preoperative chemotherapy in relation to int-GC and dif-GC and its favorable prognostic outcomes in relation to dif-GC.
| S-EPMC11807439 | biostudies-literature
Project description:[Figure: see text].
| S-EPMC8597934 | biostudies-literature
Project description:Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer.
| S-EPMC6390493 | biostudies-literature
Project description:Pure mucinous breast carcinoma (PMBC) is characterized by clusters of tumor cells floating in abundant extracellular mucin and can be classified into paucicellular (Type A) and hypercellular (Type B) subtypes. However, the clinicopathological and genomic differences between these two subtypes have not been well characterized. We retrospectively investigated the clinicopathologic features of 45 cases of surgically removed PMBC (31 Type A and 14 Type B). We also performed whole-exome sequencing (WES) in eight cases of PMBC. We found that Type B PMBC occurs at an older age and shows more aggressive clinical behavior than Type A. WES analysis revealed that HYDIN was the most frequently mutated gene in both types of PMBC. Although Type B PMBC showed a tendency toward more frequent genetic alterations, there were no statistically significant differences between the two subtypes in single nucleotide variants or insertions or deletions of bases associated with moderate or high effects. Our results provide additional evidence that PMBCs are clinicopathologically and genetically heterogeneous and lack pathognomonic genetic alterations. Further, Type B PMBC is more frequently associated with lymph node metastasis than Type A.
| S-EPMC7956951 | biostudies-literature
Project description:Mucinous breast cancer (MBC) is mainly a disease of postmenopausal women. Pure MBC is rare and augurs a good prognosis. In contrast, MBC mixed with other histological subtypes of invasive disease loses the more favorable prognosis. Because of the relative rarity of pure MBC, little is known about its cell and tumor biology and relationship to invasive disease of other subtypes. We have now developed a human breast cancer cell line called BCK4, in which we can control the behavior of MBC. BCK4 cells were derived from a patient whose poorly differentiated primary tumor was treated with chemotherapy, radiation and tamoxifen. Malignant cells from a recurrent pleural effusion were xenografted in mammary glands of a nude mouse. Cells from the solid tumor xenograft were propagated in culture to generate the BCK4 cell line. Multiple marker and chromosome analyses demonstrate that BCK4 cells are human, near diploid and luminal, expressing functional estrogen, androgen, and progesterone receptors. When xenografted back into immunocompromised cycling mice, BCK4 cells grow into small pure MBC. However, if mice are supplemented with continuous estradiol, tumors switch to invasive lobular carcinoma (ILC) with mucinous features (mixed MBC), and growth is markedly accelerated. Tamoxifen prevents the expansion of this more invasive component. The unexpected ability of estrogens to convert pure MBC into mixed MBC with ILC may explain the rarity of the pure disease in premenopausal women. These studies show that MBC can be derived from lobular precursors and that BCK4 cells are new, unique models to study the phenotypic plasticity, hormonal regulation, optimal therapeutic interventions, and metastatic patterns of MBC.
| S-EPMC3543987 | biostudies-literature
Project description:Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.
| S-EPMC9839963 | biostudies-literature
Project description:ObjectivesThe application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes.DesignFor this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists.ResultsThe model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions.ConclusionsThe findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.
| S-EPMC10373723 | biostudies-literature