Objective: To evaluate the safety of neoadjuvant therapy with pembrolizumab combined with 5-fluorouracil (5-FU) and cisplatin in patients with advanced temporal bone squamous cell carcinoma (TBSCC), and its impact on tumor response rate and disease-free survival (DFS). Methods: This prospective, single-arm, open-label clinical study enrolled patients with advanced (Stage Ⅲ/Ⅳ) TBSCC from Sun Yat-sen Memorial Hospital. Patients received 2-3 cycles of neoadjuvant therapy with pembrolizumab, 5-FU, and cisplatin, followed by definitive surgery. Postoperatively, patients received 6 cycles of pembrolizumab combined with radiotherapy. The primary endpoint was the 2-year disease-free survival (DFS) rate. Secondary endpoints included objective response rate (ORR) and safety indicators. Survival analysis was performed using the Kaplan-Meier method. Adverse events (AE) were assessed using the National Cancer Institute's Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Statistical analyses were conducted using SPSS software, version 22.0. Results: From August 2021 to April 2024, 16 patients with advanced TBSCC were enrolled (13 males and 3 females), with a median age of 54 years and a median follow-up time of 2.32 years. Following neoadjuvant therapy, the objective response rate (ORR) was 64.3% (9/14), and the disease control rate (DCR) was 92.9% (13/14). The 2-year DFS rate was 86.6%. Common treatment-related adverse events (TRAE) included leukopenia (56.3%, 9/16), nausea and vomiting (50.0%, 8/16), diarrhea, oral mucositis, and elevated liver function tests (25.0%, 4/16). One patient (6.25%) experienced a grade 3 adverse event. Conclusion: Neoadjuvant pembrolizumab-chemotherapy significantly enhances objective response rate and disease-free survival in advanced TBSCC.

Pituitary macroadenomas (PMAs) are frequently encountered tumors, predominantly characterized as soft and easily resectable during neurosurgery. In contrast, fibrous PMAs present difficulties during surgical removal. Therefore, the ability to predict the consistency of PMAs preoperatively could enhance surgical planning.To evaluate the ability of conventional T2-weighted imaging (T2WI) to predict PMA consistency by comparing isolated tumor signal intensity with the adenoma-to-middle cerebellar peduncle (ACP) ratio.Magnetic resonance imaging (MRI) scans from 45 patients with PMAs were independently reviewed by 3 blinded radiologists. For each case, the signal intensity (SI) of the adenoma and of the middle cerebellar peduncle was measured, and the ACP ratio (SI_adenoma/SI_peduncle) was calculated. Tumor consistency (soft or fibrous) was determined intraoperatively by a single neurosurgeon.Intraoperative assessment classified 29 PMAs (64.4%) as soft and 16 (35.6%) as fibrous. Isolated adenoma SI on T2WI differed significantly between soft and fibrous tumors (p = 0.013), while the ACP ratio demonstrated stronger discriminatory power (p < 0.0001). The receiver operating characteristic (ROC) curve yielded an area under the curve of 0.939 for the ACP ratio. Threshold values > 1.59 were highly predictive of soft tumors (sensitivity 72.4%; specificity 100.0%), whereas values < 1.27 were associated with fibrous tumors (sensitivity 100.0%; specificity 37.5%).Although isolated adenoma SI on T2WI showed statistical significance, it was not sufficient for consistent preoperative prediction of tumor consistency. The ACP ratio provided superior accuracy and clinical utility, supporting its role as a noninvasive imaging biomarker to enhance preoperative assessment and surgical planning in patients with pituitary macroadenomas.

Research indicates that education on sun protection and proper sunscreen application is insufficient, leading to widespread under-application of sunscreen. This lack of adherence to recommended practices increases the risk of skin cancers, photoageing and exacerbates conditions like melasma and lupus.

This review aims to provide clinicians with a practical framework to educate patients on effective sun protection, including sunscreen use, and to address common barriers to adherence.

Sunscreen is crucial for preventing melanoma, non-melanoma skin cancers, photoageing and exacerbating conditions like melasma and lupus. Despite its importance, it is often underapplied. Effective patient education is essential, and clinicians are well positioned to guide effective sunscreen use and encourage holistic sun protection behaviours. In addition to sunscreen, this includes wearing protective clothing, broad-brimmed hats, sunglasses and seeking shade.

In this issue of Developmental Cell, Zhang et al. report the identification of two transcriptionally distinct renal cell carcinoma tumor thrombus subtypes. The aggressive TT1 subtype is characterized by LOX-expressing cancer cells within an immunosuppressive microenvironment dominated by THBS2⁺ fibroblasts and GPNMB⁺ macrophages that exclude CD8⁺ T cells.

The fecal immunochemical test (FIT) is a widely used non-invasive screening method for colorectal cancer (CRC) in many countries, valued for its simplicity, affordability, and reasonable sensitivity. Typically recommended on an annual or biennial basis, the FIT is effective in reducing CRC incidence and mortality by facilitating early detection. Stool DNA tests, including multitarget DNA tests and DNA methylation assays, demonstrate higher sensitivity than FIT for CRC and advanced adenomas, although they have slightly lower specificity and higher cost. These tests are generally performed at longer intervals, such as every 3 years, and are useful alternatives for individuals who are unwilling or unable to undergo a colonoscopy. Emerging non-invasive CRC screening tools, such as liquid biopsy, microRNA, microbiome tests, and urine-based tests, are being developed to improve patient compliance and test convenience. In particular, liquid biopsy offers a minimally invasive option that may be more acceptable to populations hesitant to undergo stool-based tests. Furthermore, the integration of machine learning with metagenomic sequencing data has shown promise in distinguishing patients with CRC from healthy individuals. As CRC screening evolves, these novel approaches may enable the development of more personalized, accessible, and effective screening strategies, ultimately improving adherence and reducing CRC-related mortality.

The study examined the natural progression of squamous intraepithelial lesions (SIL) and human papilloma virus (HPV) infection during pregnancy, comparing initial and postpartum results. It also assessed delivery mode's impact on outcomes and strategies to improve follow-up care for women with abnormal cervical cancer screening results.

This retrospective study analyzed data from 59 pregnant women with SIL/positive HPV, assessing variables such as cytology, HPV status, and delivery mode. Statistical tests included Wilcoxon rank-sum and Fisher's exact tests.

The average age of patients was 29 years. Over 50% were primigravidas. A significant reduction in abnormal cytology was observed postpartum (89.83% vs. 62.50%, p = 0.009), with an increase in normal results (10.17% vs. 37.50%). No significant differences were found in HPV status (88.89% vs. 81.25%, p =0.655). Colposcopy findings were stable for 76.32% of patients between Visits 1 and 2, with 50% stability between Visits 2 and 3. Postpartum, 30.43% showed regression, while 8.70% showed progression (p = 0.017, padj < 0.050). Higher regression rates were observed after vaginal birth compared to the cesarean section (45.45% vs. 15.38%, p = 0.182) but no significant differences were found (p = 1.000). Almost 60% of patients were lost to postpartum follow-up.

Further studies with a larger population of Polish patients are needed. Cervical cancer screening should be optimized and integrated into a national registry. Pregnant patients with abnormal screening results should be managed by experts, and strategies to enhance patient compliance must be implemented.

Cancer is a leading cause of death worldwide. Early detection through screening, diagnosis, and effective management can reduce cancer mortality. Risk assessment is crucial for improving outcomes by identifying high-risk individuals based on family history, genetics, lifestyle, and environment. Such targeted screening enhances accuracy and resource efficiency. However, the complex nature of oncology data-which includes clinical observations, lab results, radiology images, treatment regimens, and genetic information-presents significant challenges for data interoperability and exchange.

This study proposes an oncology data model (ODM) based on the Fast Healthcare Interoperability Resources (FHIR) standard to facilitate the capturing, sharing, and processing of oncology data across various cancer care stages. We particularly focused on screening and risk assessment for 5 cancers: breast, cervical, esophageal, lung, and oral, within the Meghalaya Fourth Industrial Revolution for Sustainable Transformation Cancer Care pilot project in India.

The ODM incorporates data elements from a cancer patient's journey across 5 phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were modeled using the Health Level 7 FHIR Revision 4 standard. Custom FHIR profiles were developed for cancer-specific use cases, with terminology mapped to Systematized Nomenclature of Medicine-Clinical Terms, Logical Observation Identifiers Names and Codes, and the International Classification of Diseases, 10th Revision. The implementation guide (IG) was created using FHIR Shorthand, SUSHI Unshortens Short Hand Inputs, and the Health Level 7 IG Publisher. Technical and clinical validation and a stakeholder usability assessment were conducted using a demonstration tool designed for implementer training and adoption.

The data model enhances interoperability across the cancer care continuum, from screening to treatment. The resulting IG includes 25 oncology-specific resource profiles and 50 standardized terminology value sets that support both semantic and syntactic interoperability. Central to the model are the FHIR Questionnaire and QuestionnaireResponse resources, customized for structured data collection in clinical and community settings, supporting cancer screening workflows. Technical validation yielded FHIR conformance and terminology binding, while clinical validation by oncologists and public health experts confirmed the usability and relevance of 5 screening questionnaires. The demonstration tool promoted stakeholder engagement and practical evaluation of the FHIR profiles.

The FHIR-based ODM offers a unified framework for structured, interoperable cancer data exchange from screening to after treatment. This study marks the first comprehensive Indian initiative to apply FHIR standards for oncology screening and risk assessment. Integrating with national digital health systems, like the Ayushman Bharat Digital Mission, can ensure consistent data sharing across screening programs, hospitals, and registries. Future work will focus on real-world model deployment, evaluation in multiple districts, expanding to treatment and survivorship data, and promoting national adoption to inform cancer policy, research, and precision oncology efforts.

Bladder cancer diagnosis relies on cystoscopy and transurethral resection of bladder tumor (TURBT) for histopathological evaluation, but this process is time consuming, costly, and subject to variability. Optical coherence tomography (OCT) offers real-time, high-resolution imaging as a potential alternative.

This study primarily aims to assess the feasibility of capturing in vivo cross-sectional images of the bladder wall using a novel microelectromechanical systems (MEMS)-based OCT catheter. Secondary objectives include evaluating measurement duration, assessing tumor stage and grade from OCT images in comparison with histopathology, determining the catheter's ability to image resection beds, and comparing OCT-based tumor staging with white light cystoscopy assessments.

This single-center feasibility study at Amsterdam University Medical Center includes patients undergoing TURBT for suspected bladder tumors. Eligible patients must be aged 18 years or older, have at least 1 cystoscopically accessible tumor, and must be physically fit for TURBT. Exclusion criteria include pregnancy, tumors larger than 2 cm, more than 5 tumors, isolated flat lesions, or tumors solely at the bladder neck. The primary end point assesses the procedural feasibility of OCT imaging, while the secondary end points evaluate tumor staging, grading, and correlation with histopathology. Up to 25 patients will be enrolled, with feasibility achieved if diagnostic images are obtained in more than 60% of the cases. OCT imaging is performed before and after tumor resection, with histopathological results used for comparison. Patients will be monitored for adverse events for 4 weeks after the procedure, after which study participation ends.

As of November 2025, 16 participants have been enrolled, and 13 have successfully completed the study procedure. The projected end date of the study is November 2025, and results are expected to be published in March 2026.

This study is expected to provide key insights into the feasibility and clinical utility of the newly developed MEMS-based forward-looking OCT system for real-time bladder imaging during TURBT. This study will lay the groundwork for a larger trial to evaluate its diagnostic accuracy in staging and grading bladder cancer.

ClinicalTrials.gov NCT06679920; https://clinicaltrials.gov/study/NCT06679920.

DERR1-10.2196/76644.

Selecting an optimal first-line chemotherapy regimen for advanced or metastatic pancreatic cancer is challenging because of varying efficacy and toxicity profiles of fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) and gemcitabine/nab-paclitaxel (GnP). This study aimed to develop machine learning (ML) models that predict survival outcomes and guide treatment selection using routinely available clinical data.

We retrospectively analyzed 191 patients who received systemic chemotherapy for advanced or metastatic pancreatic cancer at Gangneung Asan Hospital and the Asan Medical Center between 2014 and 2023. Seventeen demographic and clinical variables, along with survival outcomes, were collected. The data set was stratified and split into training and test sets (4:1). CatBoost-based ML models were trained to predict 12-month overall survival (OS) for each regimen. A minimal subset of variables was selected using 5-fold cross-validation to optimize receiver operating characteristic (ROC)-AUC. Patients were classified as high or low risk based on model-derived thresholds.

The median age of the cohort was 62 years, and 64% was male. The ML models achieved ROC-AUCs of 0.81 for FOLFIRINOX and 0.82 for GnP. Predictive accuracies on test data were 0.77 and 0.80, respectively. Median OS differed significantly between predicted high- and low-risk groups: 9 v 15 months for FOLFIRINOX (hazard ratio [HR], 2.8; P < .0001) and 9 v 18 months for GnP (HR, 2.5; P < .01). In addition, 27% of patients predicted to be high risk for FOLFIRINOX were classified as low risk for GnP, and 32% vice versa.

ML models trained on multicenter data can effectively predict early mortality risk and help personalize chemotherapy selection in advanced or metastatic pancreatic cancer, potentially improving clinical outcomes.

The objective of this study was to develop a flexible risk stratification strategy for AML that is specific for venetoclax plus azacitidine (ven/aza), addresses real-world data (RWD) issues, and is also adaptable to different use cases.

A series of tunable risk models (RMs) were generated from a dynamic counterfactual machine learning (ML) strategy. These used a range of features from diagnostic AML samples and were tested using objective metrics on a single-institution cohort of 316 newly diagnosed patients treated with ven/aza. RM performance was tested using various model assumptions, data elements, and end points and with applications to an external AML real-world cohort (RWC).

Favorable, intermediate, and adverse risk groups were identified in a series of ML-based RMs using different assumptions, for genetic-only or genetic-plus-phenotypic data elements and with overall survival and event-free survival as end points. Most RMs demonstrated equitable patient distribution (approximately 20%-40% in each risk group), significant separation between risk strata (log-rank-based P values <0.001), and predictability computed by time-dependent survival AUC values of 0.60-0.70. Similar performance was observed when the proposed RM strategy was adapted and compared with the European Leukemia Net 2022 using the external RWC.

The proposed ML strategy addresses a variety of RWD considerations and is readily tunable through coding and parameter updates for different contexts and use case needs. This strategy represents a novel approach to developing more effective RMs for AML and possibly other diseases.