Chemotherapy

Interpretable machine learning for postoperative nausea and vomiting prediction in elderly orthopedic patients: a comparative study.

BACKGROUND: Postoperative nausea and vomiting (PONV) prolongs hospitalization and reduces patient satisfaction. Identifying high-risk elderly patients requires accurate absolute risk assessments, yet existing tools often lack probability calibration and transparency. METHODS: We included 1216 elderly patients undergoing elective hip or knee surgery. To strictly prevent data leakage, the dataset wa...

Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.

PURPOSE: High-risk stage II colorectal cancer (CRC) shows heterogeneous outcomes despite adjuvant chemotherapy. We developed and validated an interpretable multimodal deep learning model integrating clinical data, serum biomarkers, and venous-phase CT to predict 5-year CRC-specific mortality in high-risk stage II CRC. METHODS: This retrospective, multicenter cohort included 778 high-risk stage II ...

A prognostic model based on nucleotide metabolism genes in osteosarcoma.

Osteosarcoma typically arises during adolescence, posing a significant challenge. Despite comprehensive treatment strategies encompassing surgery, rad...

Artificial Intelligence-Based Identification of Common Canine Skin Lesions From Clinical Images.

BACKGROUND: Accurate evaluation of skin lesions is an essential component of dermatological examination, yet it can be time-consuming and subject to i...

Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning.

BACKGROUND: This study sought to develop an innovative body composition (BC)-based deep learning (DL) model to precisely evaluate survival in gastric ...

Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation.

BACKGROUND/AIM: Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response ...

Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System.

BACKGROUND: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of lon...

[Current applications and prospects of artificial intelligence in perioperative comprehensive management for gastrointestinal surgery].

The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to adva...

Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Microsatellite instability (MSI) has emerged as a key predictive biomarker for chemotherapy and immunotherapy response, and as a prognostic...

Graph augmented transformers improve chemotherapy toxicity symptom extraction from clinical notes.

Chemotherapy is essential for cancer treatment but may cause adverse events requiring emergency department visits and hospitalizations, placing substa...

Adjuvant databases and knowledge bases for data-driven research: a computational biology perspective.

In adjuvant research, which has long relied on experience and trial and error, advancements in technologies in broad areas, including artificial intel...

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...

Integrating multi-omics data for enhanced prognosis prediction in gastric cancer post-neoadjuvant therapy.

BACKGROUND: Neoadjuvant therapy is crucial for locally advanced gastric cancer (LAGC), yet response varies significantly. Traditional models based on ...

Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study.

BACKGROUND: Electrolyte abnormalities following chemotherapy are common and clinically significant complications in cancer patients and are often asso...

Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis.

Age is a well-known risk factor to develop severe viral respiratory infections, including severe COVID-19. This study aimed to identify the biological...

Nanomaterials reshaping cancer radiotherapy: Radiosensitization mechanisms, delivery and theranostic platforms, multimodal synergy, and clinical translation strategies.

Radiotherapy (RT) remains a cornerstone of cancer management but is fundamentally constrained by normal tissue toxicity, intrinsic and acquired radior...

Isolated Nausea and Pretibial Rash in a Returning Traveler: Forme Fruste of Leptospirosis.

Leptospirosis is difficult to diagnose because of protean nonspecific clinical manifestations and the lack of rapid, actionable laboratory testing. A ...

Development and validation of an ensemble machine learning model to predict survival in locally advanced rectal cancer: A multicenter, retrospective study.

BACKGROUND: Patients identified with locally advanced rectal cancer (LARC) demonstrate a varied prognosis after undergoing neoadjuvant chemoradiothera...

Optimal treatment selection for hepatocellular carcinoma in the era of immunotherapy.

Immunotherapy has revolutionized hepatocellular carcinoma (HCC) management, necessitating personalized strategies in current guidelines. Despite curat...

Advancing precision medicine in pancreatic cancer using novel biomarkers and clinical targets.

Precision-guided therapy is imperative in the battle against pancreatic ductal adenocarcinoma (PDAC), one of the most lethal solid malignancies with l...