Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies.

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. METHODS: We introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression. RESULTS: We observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC. CONCLUSIONS: CollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.

The role of cells that are part of our immune system in altering the structure of the protein collagen within cancers is not fully understood, particularly within cancers that affect women such as ovarian, cervical and uterine cancers. Here, we developed a computer-based method called CollaTIL to explore how immune cells influence collagen in these tumors and affect their growth. We found that a higher presence of immune cells leads to less organized collagen in the tumor. Conversely, when there are fewer immune cells, the collagen tends to be more structured. Additionally, CollaTIL identifies patterns that predict patient outcomes in these cancers. These findings not only enhance our understanding of tumor biology but also may be useful in helping clinicians to predict which patients are at risk of their disease progressing.

Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity.

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. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.

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. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

(F)utility of computed tomography of the chest in the presence of pleural effusion.

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. The objective of the study was to assess the clinical benefit of an additional chest computed tomography (CT) scan over plain chest X-ray alone in the management of patients with pleural effusion. METHODS: Retrospective analysis in 94 consecutive patients with pleural effusion who underwent chest X-ray and CT scan over an 18-month period in a single institution. All chest X-ray and CT scan reports were compared and correlated with clinical parameters in order to assess their utility in the clinical management. No blinding was applied. RESULTS: In 75 chest CT scan reports (80 %), information provided by the radiologist did not change clinical management when compared to plain chest X-ray alone and did not provide any additional information over chest X-ray. Only 2/49 (4 %) of the native chest CT scan reports provided clinically relevant information as compared to 17/45 (38 %) contrast-enhanced chest CT scan reports (p<0.001). CONCLUSIONS: In this retrospective cohort of patients with pleural effusion, an additional chest CT scan was not useful in the majority of patients. However, if a chest CT scan is required, then a contrast-enhanced study after pleural aspiration should be performed. Further prospective studies are required to confirm these findings.

Current morphologic criteria perform poorly in identifying hereditary leiomyomatosis and renal cell carcinoma syndrome-associated uterine leiomyomas.

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. Regarding hereditary predisposition, morphology-based genetic referral systems in some instances have eclipsed genealogy-based systems, for example, cell type in ovarian cancer and BRCA screening. In other instances such as Lynch syndrome, morphology-based schemas supplement clinical schemas and there is an emerging standard of care for reflex biomarker testing. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome predisposes patients to uterine and cutaneous leiomyomas (LMs) and renal cell carcinomas (RCCs). Several authors have emphasized the role pathologists may play in identifying this syndrome by recognizing the morphologic characteristics of syndromic uterine LMs and RCCs. Recently immunohistochemical overexpression of S-(2-succinyl) cysteine (2SC) has been demonstrated as a robust biomarker of mutation status in tumors from HLRCC patients. In this blinded control-cohort study we demonstrate that the proposed morphologic criteria used to identify uterine LMs in HLRCC syndrome are largely irreproducible among pathologists and lack sufficient robustness to serve as a trigger to triage cases for 2SC immunohistochemistry or patients for further family/personal history inquiry. Although refinement of morphologic criteria can be considered, in view of the availability of a clinically robust biomarker, consideration should be given to reflex testing of uterine LMs with an appropriate age cut off or in the setting of a suspicious family history.

A rare solitary fibrous tumour of kidney.

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. We report a case of a large solitary fibrous tumour clinically and radiologically thought to be renal cell carcinoma arising in the kidney of a 30 year old female. The radical nephrectomy was performed. The tumour was a well- circumscribed, solid mass attached to the renal pelvis without necrosis and haemorrhage. Histopathologically, a spindle cell neoplasia with alternating hypo and hypercellular areas, storiform, fascicular and hemangipericytoma like growth pattern and less cellular dense collagen deposits were observed. Immunohistochemical studies revealed reactivity for CD34, CD99 and Bcl-2 protein.