Publications by authors named "Tilak Pathak"

Article Synopsis
  • Machine learning, particularly deep learning with convolutional neural networks (CNNs), is being used to detect prostate cancer in tissue slides, but sample type differences affect model accuracy.
  • Research tested whether CNNs trained on one type of sample (biopsy or radical prostatectomy) could effectively analyze the other type, revealing a significant drop in performance across sample types.
  • Results indicated that models performed well on their own sample but poorly on the alternative type, highlighting the need to consider morphological differences in training to improve cancer detection accuracy in clinical settings.*
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Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with a disease recurrence rate of around 20%. Lymphoid formations, which occur in nonlymphoid tissues during chronic inflammatory, infectious, and immune responses, have been linked with tumor suppression. Lymphoid aggregates potentially enhance the body's antitumor response, offering an avenue for attracting tumor-infiltrating lymphocytes and fostering their coordination.

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Background: The changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy are a complex area of study. Previous research underscores the importance of investigating the immune and collagen components within the tumor microenvironment for prognostic implications.

Methods: In this study, we utilized computational pathology techniques with Hematoxylin and Eosin-stained images to quantitatively characterize the immune and collagen architecture within the tumor microenvironment of patients with high-grade serous ovarian carcinoma.

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Background: The role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited.

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Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient's outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers.

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Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes.

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Background: Pleural effusion is common and can cause significant morbidity. The chest X-ray is often the initial radiological test, but additional tests may be required to reduce uncertainty and to provide additional diagnostic information. However, additional exposure and unnecessary costs should be prevented.

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The contemporary oncologic pathology report conveys diagnostic, prognostic, predictive, and hereditary predisposition information. Each component may be premised on a morphologic feature or a biomarker. Clinical validity and reproducibility are paramount as is standardization of reporting and clinical response to ensure individualization of patient care.

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A solitary fibrous tumour is an unusual spindle cell neoplasm. It frequently arises from the serosal surface of pleural cavity but has recently been described in diverse extrapleural sites. Urogenital localization is rare and only 36 cases of solitary fibrous tumours of the kidney have been described on published report.

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