Fibrosis-Encapsulated Tumoroid, A Solid Cancer Assembloid Model for Cancer Research and Drug Screening.

Peritumoral fibrosis is known to promote cancer progression and confer treatment resistance in various solid tumors. Consequently, developing accurate cancer research and drug screening models that replicate the structure and function of a fibrosis-surrounded tumor mass is imperative. Previous studies have shown that self-assembly three-dimensional (3D) co-cultures primarily produce cancer-encapsulated fibrosis or maintain a fibrosis-encapsulated tumor mass for a short period, which is inadequate to replicate the function of fibrosis, particularly as a physical barrier.

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process.

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer.

Background: The lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography-a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer.

Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.

Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network.

CD24 induced cellular quiescence-like state and chemoresistance in ovarian cancer cells via miR-130a/301a-dependent CDK19 downregulation.

Cancer stem-like cell (CSC) is thought to be responsible for ovarian cancer recurrence. CD24 serves as a CSC marker for ovarian cancer and regulates the expression of miRNAs, which are regulators of CSC phenotypes. Therefore, CD24-regulated miRNAs may play roles in manifesting the CSC phenotypes in ovarian cancer cells.

Deciphering the Role of ERBB3 Isoforms in Renal Cell Carcinoma: A Comprehensive Genomic and Transcriptomic Analysis.

ERBB3, a key member of the receptor tyrosine kinase family, is implicated in the progression and development of various human cancers, affecting cellular proliferation and survival. This study investigated the expression of ERBB3 isoforms in renal clear cell carcinoma (RCC), utilizing data from 538 patients from The Cancer Genome Atlas (TCGA) Firehose Legacy dataset. Employing the SUPPA2 tool, the activity of 10 ERBB3 isoforms was examined, revealing distinct expression patterns in RCC.

Deep learning-based diagnosis of lung cancer using a nationwide respiratory cytology image set: improving accuracy and inter-observer variability.

Deep learning (DL)-based image analysis has recently seen widespread application in digital pathology. Recent studies utilizing DL in cytopathology have shown promising results, however, the development of DL models for respiratory specimens is limited. In this study, we designed a DL model to improve lung cancer diagnosis accuracy using cytological images from the respiratory tract.

MET overexpression in ovarian cancer via CD24-induced downregulation of miR-181a: A signalling for cellular quiescence-like state and chemoresistance in ovarian CSCs.

Increased expression of CD24 and MET, markers for cancer stem-like cells (CSCs), are each associated with ovarian cancer severity. However, whether CD24 and MET are co-expressed in ovarian CSCs and, if so, how they are related to CSC phenotype manifestation remains unknown. Our immunohistochemistry analysis showed that the co-expression of CD24 and MET was associated with poorer patient survival in ovarian cancer than those without.

Beyond the Microscope: A Technological Overture for Cervical Cancer Detection.

Cervical cancer is a common and preventable disease that poses a significant threat to women's health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health Organization. Early detection and diagnosis of cervical cancer are crucial for reducing mortality and morbidity rates.

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer.

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.

Introduction: Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.

Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism.

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise.

Androgen-Independent Prostate Cancer Is Sensitive to CDC42-PAK7 Kinase Inhibition.

Prostate cancer is a common form of cancer in men, and androgen-deprivation therapy (ADT) is often used as a first-line treatment. However, some patients develop resistance to ADT, and their disease is called castration-resistant prostate cancer (CRPC). Identifying potential therapeutic targets for this aggressive subtype of prostate cancer is crucial.

Characteristics of BRCA2 Mutated Prostate Cancer at Presentation.

Genetic alterations of DNA repair genes, particularly BRCA2 in patients with prostate cancer, are associated with aggressive behavior of the disease. It has reached consensus that somatic and germline tests are necessary when treating advanced prostate cancer patients. Yet, it is unclear whether the mutations are associated with any presenting clinical features.

Lysophosphatidic acid-induced amphiregulin secretion by cancer-associated fibroblasts augments cancer cell invasion.

Cancer-associated fibroblasts (CAFs) are key structural components of the tumor microenvironment and are closely associated with tumor invasion and metastasis. Lysophosphatidic acid (LPA) is a biolipid produced extracellularly and involved in tumorigenesis and metastasis. LPA has recently been implicated in the education and transdifferentiation of normal fibroblasts (NFs) into CAFs.

p53 Immunohistochemistry and Mutation Types Mismatching in High-Grade Serous Ovarian Cancer.

High-grade serous carcinoma (HGSCa) of the ovary is featured by gene mutation. Missense or nonsense mutation types accompany most cases of HGSCa that correlate well with immunohistochemical (IHC) staining results-an all (missense) or none (nonsense) pattern. However, some IHCs produce subclonal or mosaic patterns from which TP53 mutation types, including the wild type of the gene, cannot be clearly deduced.

Metabolic tumour volume on F-FDG PET/CT predicts extended pathological T stages in patients with renal cell carcinoma at staging.

We evaluated the predictive value of F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/CT (PET/CT) for extended pathological T (pT) stages (≥ pT3a) in Renal cell carcinoma (RCC) patients at staging. Thirty-eight RCC patients who underwent F-FDG PET/CT at staging, followed by radical nephrectomy between September 2016 and September 2018, were included in this prospective study. Patients were classified into two groups (limited pT stage: stage T1/2, n = 17; extended pT stage: T3/4, n = 21).

Differences in genomic profile of high-grade urothelial carcinoma according to tumor location.

Objectives: To establish targeted therapies based on the molecular landscape in upper urinary tract urothelial carcinoma (UTUC), we tried to investigate the molecular characteristics of UTUC compared with those of bladder urothelial carcinoma (BLUC) by next-generation sequencing (NGS).

Materials And Methods: We selected 71 high-grade infiltrating urothelial carcinoma tissue specimens from 33 UTUC and 38 BLUC patients. NGS analysis was performed with the Illumina TruShigt Oncology-500 panel.

Revisiting extraprostatic extension based on invasion depth and number for new algorithm for substaging of pT3a prostate cancer.

Extraprostatic extension (EPE) is a factor in determining pT3a stage in prostate cancer. However, the only distinction in EPE is whether it is focal or non-focal, causing diagnostic and prognostic ambiguity. We substaged pT3a malignancies using classification of EPE to improve personalized prognostication.

Immunohistochemical and genetic characteristics of HPV-associated endocervical carcinoma with an invasive stratified mucin-producing carcinoma (ISMC) component.

Invasive stratified mucin-producing carcinoma (ISMC) is a recently described entity of human papillomavirus (HPV)-associated endocervical adenocarcinoma with phenotypic plasticity and aggressive clinical behavior. To identify the cell of origin of ISMC, we investigated the immunohistochemical expression of cervical epithelial cell markers (CK7, PAX8, CK5/6, p63, and CK17), stemness markers (ALDH1 and Nanog), and epithelial-mesenchymal transition (EMT) markers (Snail, Twist, and E-cadherin) in 10 pure and mixed type ISMCs with at least 10% of ISMC component in the entire tumor, seven usual type endocervical adenocarcinomas (UEAs), and seven squamous cell carcinomas (SCCs). In addition, targeted sequencing was performed in 10 ISMCs.

Prostate epithelial genes define therapy-relevant prostate cancer molecular subtype.

Background And Objectives: Transcriptomic landscape of prostate cancer (PCa) shows multidimensional variability, potentially arising from the cell-of-origin, reflected in serum markers, and most importantly related to drug sensitivities. For example, Aggressive Variant Prostate Cancer (AVPC) presents low PSA per tumor burden, and characterized by de novo resistance to androgen receptor signaling inhibitors (ARIs). Understanding PCa transcriptomic complexity can provide biological insight and therapeutic guidance.

Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering.

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively.

Cancer-associated fibroblasts induce an aggressive phenotypic shift in non-malignant breast epithelial cells via interleukin-8 and S100A8.

Cancer-associated fibroblasts (CAFs) in the tumor microenvironment have been associated with tumor progression in breast cancer. Although crosstalk between breast cancer cells and CAFs has been studied, the effect of CAFs on non-neoplastic breast epithelial cells is not fully understood to date. Here, we investigated the effect of CAFs on aggressive phenotypes in non-neoplastic MCF10A breast epithelial cells.