Rare Breast Cancer Subtypes

Jenkins, Sarah M.; Kachur, Megan E; Rechache, Kamil; Wells, Justin; Lipkowitz, Stanley

doi:10.1007/s11912-021-01048-4

Cited by 32 publications

(29 citation statements)

References 151 publications

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“…The rare subtypes are difficult to diagnose using hematoxylin and eosin staining, and they include the following: follicular thyroid carcinoma (∼0.9–10.8%), medullary thyroid cancer (approximately 0.6–2.2%), and undifferentiated carcinoma (approximately 0.1%) ( Lim et al, 2017 ; Zhao et al, 2019 ; He and Wei, 2021 ). Similar phenomena exist in lung cancer ( de Sousa and Carvalho, 2018 ; Shirsat et al, 2021 ) and breast cancer ( Jenkins et al, 2021 ), among others. The pathological image data from the rare categories are difficult to obtain because of their low incidence rate, which results in low diagnostic consistency among pathologists and improperly building a convolutional neural network (CNN) model directly.…”

Section: Introductionsupporting

confidence: 72%

Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images

Zhu

Chen

Guo

et al. 2022

Front. Bioeng. Biotechnol.

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Introduction: The pathological rare category of thyroid is a type of lesion with a low incidence rate and is easily misdiagnosed in clinical practice, which directly affects a patient’s treatment decision. However, it has not been adequately investigated to recognize the rare, benign, and malignant categories of thyroid using the deep learning method and recommend the rare to pathologists.Methods: We present an empirical decision tree based on the binary classification results of the patch-based UNet model to predict rare categories and recommend annotated lesion areas to be rereviewed by pathologists.Results: Applying this framework to 1,374 whole-slide images (WSIs) of frozen sections from thyroid lesions, we obtained an area under a curve of 0.946 and 0.986 for the test datasets with and without WSIs, respectively, of rare types. However, the recognition error rate for the rare categories was significantly higher than that for the benign and malignant categories (p < 0.00001). For rare WSIs, the addition of the empirical decision tree obtained a recall rate and precision of 0.882 and 0.498, respectively; the rare types (only 33.4% of all WSIs) were further recommended to be rereviewed by pathologists. Additionally, we demonstrated that the performance of our framework was comparable to that of pathologists in clinical practice for the predicted benign and malignant sections.Conclusion: Our study provides a baseline for the recommendation of the uncertain predicted rare category to pathologists, offering potential feasibility for the improvement of pathologists’ work efficiency.

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Section: Introductionsupporting

confidence: 72%

Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images

Zhu

Chen

Guo

et al. 2022

Front. Bioeng. Biotechnol.

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“…Because it is rare, specific management guidelines have not been developed. 9 Having to screen a large number of potential study participants to enroll those with the specific biological marker contributes to the inefficiency of study conduct.…”

Section: S17mentioning

confidence: 99%

Regulatory Framework for Drug Development in Rare Diseases

Korth‐Bradley

2022

The Journal of Clinical Pharma

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Drug development is a highly regulated industry. Therapeutic options for rare diseases must meet the same high standards for the demonstration of safety and efficacy as do those for more common diseases. The approval of the Orphan Drug Act in 1983 has resulted in many more resources for preclinical research, the standardization of patient registries, and the use of real‐world data, among other measures, that, along with the advances in drug development, has resulted in the approval of therapies for some of the most unusual diseases. Increased attention to the diagnosis and treatment of rare diseases has also accelerated the development of gene therapies that may offer significant amelioration and even cures for such diseases in the near future. Rare diseases disproportionately affect children, with severe and debilitating effects. Few effective treatments are available for most rare diseases. To avoid the unnecessary waste of data collected in studies of these patients, and to promote efficient drug development, there is a growing collaboration among patient communities, investigators, clinicians, sponsors, and regulatory authorities. All interested parties are working together to identify the most appropriate research questions and move quickly to make available safe and effective treatments. This article is a survey of the most commonly used regulatory remedies that have been put in place to serve as a framework for drug development in rare diseases.

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“…IMPC is aggressive in groups [ 2 , 3 ]. The unique clustered growth pattern and aggressive biological behavior of IMPC make it more invasive and prone to vascular invasion and lymph node metastasis [ 4 , 5 ]. Previous studies have shown that even if the tumor is less than 2 cm, and the proportion of tumor tissue is less than 25%, the number of lymphatic invasions, lymph node metastasis, and lymph node metastasis of IMPC is significantly higher than that of invasive ductal carcinoma.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Prognostic Factors and Construction of Prognostic Models for Invasive Micropapillary Carcinoma of the Breast

Wang¹,

Yan²

2022

Computational and Mathematical Methods in Medicine

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Objective. To compare and analyze the clinical characteristics of invasive micropapillary carcinoma (IMPC) of the breast (IMPC-B) and invasive ductal carcinoma (IDC) of the breast (IDC-B) and establish a prognostic model of IMPC-B. Methods. We retrospectively analyzed data for patients diagnosed with breast cancer in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018 and screened 581 patients with IMPC and 1325 patients with IDC. We compared age, race, laterality, tumor site, histological grade, type of surgery, radiation, chemotherapy, whether the first primary tumor, T stage, N stage, M stage, and molecular type between IMPC-B and IDC-B and draw survival curves of IMPC-B and IDC-B. The relationship between clinical factors and prognosis was investigated by univariate analysis using the Log-rank test and multivariate analysis of the Cox proportional hazards regression model. A risk scoring model was constructed based on independent risk factors to distinguish high-risk and low-risk patients; in addition, a nomogram was created to predict patient survival. Results. There were differences between the two groups in the age of onset, race, tumor site, histological grade, type of surgery, N stage, and molecular type ( p < 0.05 ). Overall survival was decreased in IMPC-B compared with IDC-B ( p < 0.05 ). The prognosis of IMPC-B was significantly correlated with histological grade, whether the first primary tumor, type of surgery, radiotherapy, chemotherapy, T stage, and N stage. Based on the relationship between the above factors and overall survival prognosis, the risk score model we constructed can effectively distinguish high-risk and low-risk patients ( p < 0.05 ). The established nomogram had better performance in predicting survival in patients with IMPC-B ( C − index = 0.78 ). Conclusion. IMPC-B has a worse prognosis than IDC-B, with earlier age of onset, higher histological grade, and later N stage, and luminal breast cancer is the main type. The nomogram can well predict the prognosis of patients with IMPC-B, which has a high clinical reference value and provides a scientific basis for clinical treatment.

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Rare Breast Cancer Subtypes

Cited by 32 publications

References 151 publications

Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images

Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images

Regulatory Framework for Drug Development in Rare Diseases

Analysis of Prognostic Factors and Construction of Prognostic Models for Invasive Micropapillary Carcinoma of the Breast

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