Mastering rectal cancer MRI: From foundational concepts to optimal staging.

MRI plays a critical role in the local staging, restaging, surveillance, and risk stratification of patients, ensuring they receive the most tailored therapy. As such, radiologists must be familiar not only with the key MRI findings that influence management decisions but also with the appropriate MRI protocols and structured reporting. Given the complexity of selecting the optimal therapy for each patient-which often requires multidisciplinary discussions-radiologists should be well-versed in relevant treatment strategies and surgical terms, understanding their significance in guiding patient care.

Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.

Background: Colorectal cancer (CRC) presents significant challenges in chemotherapy response prediction due to its molecular heterogeneity. Current methods often fail to account for the complexity and variability inherent in individual tumors.

Methods: We developed a novel approach using matched CRC tumor and organoid gene expression data.

Anorectal mucosal melanoma.

Anorectal mucosal melanoma accounts for less than 1 % of all anorectal malignant tumors and a tendency for delayed diagnosis leads to advanced disease at presentation. Due to the rarity of the disease, there are limited prospective trials exploring the optimal treatment strategies. Generally, tumors are surgically excised, with a preference for conservative management with wide local excision.

Discovery of Oncogenic Mediator Genes in Rectal Cancer Chemotherapy Response using Gene Expression Data from Matched Tumor and Patient-Derived Organoid.

Rectal cancer (RC) presents significant treatment challenges, particularly in the context of chemotherapy resistance. Addressing this, our study pioneers the use of matched RC tumor tissue and patient-derived organoid (PDO) models coupled with the innovative computational tool, Moonlight, to explore the gene expression landscape of RC tumors and their response to chemotherapy. We analyzed 18 tissue samples and 32 matched PDOs, ensuring a high-fidelity representation of the tumor bioloy.

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection.

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU).

Watch-and-Wait Approach to Rectal Cancer: The Role of Imaging.

The diagnosis and treatment of rectal cancer have evolved dramatically over the past several decades. At the same time, its incidence has increased in younger populations. This review will inform the reader of advances in both diagnosis and treatment.

Effect of Long-Term Mesalamine Therapy on Cancer-Associated Gene Expression in Colonic Mucosa of Patients with Ulcerative Colitis.

Background: The role of 5-aminosalicylic acid (5-ASA or mesalamine) in the prevention of colorectal cancer in ulcerative colitis (UC) patients was reported, but the effect on molecular targets in UC colon mucosa is unknown.

Aim: This observational study evaluates gene expression levels of 5-ASA targets using serial colon biopsy specimens from UC patients undergoing long-term 5-ASA therapy.

Methods: Transcript levels were compared between colonoscopic biopsy specimens collected from 62 patients at initial and final follow-up colonoscopy at 2-6 years.