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Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.
Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response h…
FULLTEXT: Machine-learning randomized randomized -
Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning.
Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it remain underexplored. To address this issue, we analyzed the Gene Expression Omnibus (GEO) a…
FULLTEXT: Machine-learning randomized randomized -
Genetic relationships between the gut microbiota and prostate cancer: Mendelian randomization combined with bioinformatics analysis.
[BACKGROUND] Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. [METHODS] We screened genetic instruments related to the gut m…
FULLTEXT: Machine-learning randomized randomized -
Development and validation of machine learning prognostic models for overall survival in non-surgical prostate cancer patients with bone metastases.
[OBJECTIVE] To construct and interpret a machine learning model for predicting overall survival in nonsurgical prostate cancer with bone metastases (PCBM). [METHODS] Data from 3,378 SEER database patients were utilized to develop machine l…
FULLTEXT: Machine-learning randomized randomized -
Prolonged progression-free survival with zanubrutinib in relapsed/refractory CLL: an indirect treatment comparison versus other BTK inhibitors using multilevel network meta-regression.
[BACKGROUND] Bruton tyrosine kinase inhibitors (BTKis) are therapeutic agents for relapsed/refractory chronic lymphocytic leukemia (R/R CLL). Previous indirect treatment comparisons are limited in simultaneously comparing multiple intervent…
FULLTEXT: Machine-learning randomized randomized -
The surgery for the patients with intestinal non‑Hodgkin lymphomas: a nationwide study.
[BACKGROUND] The treatment strategy for intestinal non-Hodgkin lymphoma (NHL) and the role of surgery warrant reevaluation. [METHODS] This study analyzed clinical data from a cohort of 12,047 patients diagnosed with intestinal NHL, extract…
FULLTEXT: Machine-learning randomized randomized -
Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning.
[OBJECTIVE] This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). [METHODS] Differentially expressed N…
FULLTEXT: Machine-learning randomized randomized -
A Randomized Trial Evaluating Intraoperative Ischemic Preconditioning of Parathyroid Glands During Total Thyroidectomy: A Signal for Earlier Parathyroid Function Recovery.
[BACKGROUND] Ischemic preconditioning of parathyroid glands (IPCP) is biologically plausible but clinical evidence is limited. In this single-center randomized trial (ChiCTR2000039788), we compared IPCP versus control during total thyroidec…
FULLTEXT: Machine-learning randomized randomized -
Comparison of the effects of exercise-based interventions on cardiometabolic health and fatigue in men with prostate cancer receiving androgen deprivation therapy: A network meta-analysis.
[OBJECTIVE] To compare the effects of exercise-based interventions on cardiometabolic health and fatigue in men with prostate cancer receiving androgen deprivation therapy (ADT). [METHODS] A comprehensive search for Randomized controlled s…
FULLTEXT: Machine-learning randomized randomized -
Impact of offering blood-based testing alongside existing modalities for colorectal cancer screening among those who previously declined screening: an economic evaluation.
[AIM] Inadequate adherence to colorectal cancer screening reduces individual and population level health benefits. Blood-based tests offer a new modality that may help patients overcome barriers, but there are concerns about the impact of p…
FULLTEXT: Machine-learning randomized randomized -
A vision-language model-based approach for lung cancer diagnosis using lossless 3D CT images: evaluation of GPT-4.1 and GPT-4o for patient-level malignancy assessment.
[PURPOSE] Large vision-language models (VLMs), such as GPT-4.1 and GPT-4o, have shown strong potential in medical tasks. However, their application in lossless 3D medical image analysis is still underexplored. This study proposes a GPT-base…
FULLTEXT: Machine-learning randomized randomized -
Predicting lung cancer survival with attention-based CT slices combination.
Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these…
FULLTEXT: Machine-learning randomized randomized -
Patient-reported outcomes with a personalized follow-up program after lung cancer resection: A single-center randomized controlled trial.
[OBJECTIVE] This study aims to determine the impact of PROM with a personalized follow-up program on the evaluation of quality of life and self-management for patients after lung cancer resection. [METHODS] Given a formal power calculation…
FULLTEXT: Machine-learning randomized randomized -
Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.
[BACKGROUND] Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But …
FULLTEXT: Machine-learning randomized randomized -
Cost-effectiveness of pembrolizumab plus chemotherapy for metastatic non-small cell lung cancer: a head-to-head trial vs. real-world comparison.
[OBJECTIVES] Real-world evidence (RWE) is increasingly used in health technology assessment (HTA) to address uncertainties surrounding the generalizability of randomized clinical trial (RCT) data. Pembrolizumab plus platinum-based chemother…
FULLTEXT: Machine-learning randomized randomized -
Managing psychological distress in women with breast cancer: A systematic review of intervention trends in the past decade.
[OBJECTIVE] The rising incidence and survival rates of breast cancer have increased the number of breast cancer survivors (BCSs) experiencing psychological distress that often overlaps with anxiety, depression, and fear of recurrence. Many …
FULLTEXT: Machine-learning randomized randomized -
A data fusion deep learning approach for accurate organelle-based classification of cancer cells.
[PURPOSE] Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preproce…
FULLTEXT: Machine-learning randomized randomized -
Systematic meta-analysis of the toxicities and side effects of the targeted drug lenvatinib.
[BACKGROUND] Lenvatinib, an effective targeted drug for various cancers, has clinical medication safety concerns due to its toxicities and side effects. [OBJECTIVE] This study evaluated lenvatinib-induced any adverse events (any AEs) and n…
FULLTEXT: Machine-learning randomized randomized -
Robotic-assisted versus standard laparoscopic surgery for colorectal cancer in obese patients: a systematic review and meta-analysis.
Colorectal cancer represents a major global health concern and obesity adds complicates its surgical management. This meta-analysis aimed to evaluate the comparative effectiveness and safety of robotic-assisted surgery and standard laparosc…
FULLTEXT: Machine-learning randomized randomized -
Molecular targeted therapy in combination with chemotherapy for the treatment of platinum-resistant/refractory ovarian cancer (PROC): a systematic review and network meta-analysis.
[BACKGROUND] Although single-agent chemotherapy is the most common approach for treating platinum-resistant or refractory ovarian cancer (PROC), there is growing evidence that combining molecular targeted agents with chemotherapy is benefic…
FULLTEXT: Machine-learning randomized randomized