Cancer Ai

OBJECTIVE: Aniline poisoning is considered to be an important factor mediating the development and progression of male bladder cancer,and long non-coding RNA(lncRNA)has also been shown to affect the prognosis of male bladder cancer.Therefore,this study intended to screen and identify lncrnas associated with highly sensitive aniline poisoning of male bladder cancer,and to construct a tumor risk prediction model accordingly. METHODS: Gene expression and clinical data from 410 tissues were downloaded from the Cancer Genome Atlas(TCGA),and all samples were randomly divided into training and testing groups...

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression...

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV ) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available...

Lymph node metastasis (LNM) detection can be automated using artificial intelligence-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer. The aim of this study was to develop of a clinical-grade digital pathology tool for LNM detection in colorectal cancer (CRC) using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from five pathology departments digitized by four different scanning systems...

The novel metal(II)-based complexes HA-Cu, HA-Co, and HA-Ni with phenanthroline, sulfamethazine, and aromatic-aromatic coupled disulfamethazines as ligands were synthesized and characterized. HA-Cu, HA-Co, and HA-Ni all showed a broad spectrum of cytotoxicity and antiangiogenesis. HA-Cu was superior to HA-Co and HA-Ni, and even superior to DDP, showing significant inhibitory effect on the growth and development of tripe-negative breast cancer in vivo and in vitro. HA-Cu exhibited observable synergistic effects of antiproliferation, antiangiogenesis, anti-inflammatory, pro-apoptosis, and cuproptosis to effectively inhibited tumor survival and development...

In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes•An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections.•A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset.•Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell...

PET scans provide additional clinical value but are costly and not universally accessible. Salehjahromi et al.1 developed an AI-based pipeline to synthesize PET images from diagnostic CT scans, demonstrating its potential clinical utility across various clinical tasks for lung cancer.

PURPOSE: To evaluate the utility of tumor content in circulating cell-free DNA (ccfDNA) for monitoring hepatocellular carcinoma (HCC) throughout its natural history. METHODS: We included 67 hepatitis B virus (HBV)-related HCC patients, of whom 17 had paired pre- and post-treatment samples, and 90 controls. Additionally, in a prospective cohort with HBV surface antigen-positive participants recruited in 2012 and followed up biannually with blood sample collections until 2019, we included 270 repeated samples before diagnosis from 63 participants who later developed HCC (pre-HCC samples)...

Lung adenocarcinoma (LUAD) is the predominant subtype within the spectrum of lung malignancies. CTHRC1 has a pro-oncogenic role in various cancers. Here, we observed the upregulation of CTHRC1 in LUAD, but its role in cisplatin resistance in LUAD remains unclear. Bioinformatics analysis was employed to detect CTHRC1 and SRY-related HMG-box 4 (SOX4) expression in LUAD. Gene Set Enrichment Analysis predicted the enriched pathways related to CTHRC1. JASPAR and MotifMap databases predicted upstream transcription factors of CTHRC1...

BACKGROUND: Endocrine therapy (ET) adherence leads to increased survival in breast cancer (BC). How follow-up should be done to maximize adherence is not known. OBJECTIVES: To assess adherence to ET, factors favouring adherence to ET and effects on survival in a population-based cohort of BC patients in western Sweden. DESIGN: This is a retrospective study. METHODS: We included 358 patients operated for oestrogen receptor-positive BC and recommended 5 years of ET, in Region Halland, Sweden, year 2015 to 2016...

Triple-negative breast cancer (TNBC), the most aggressive subtype of breast cancer, has a poor prognosis and lacks effective treatment strategies. Here, the study discovered that TNBC shows a decreased expression of epithelial transcription factor ovo-like 2 (OVOL2). The loss of OVOL2 promotes fatty acid oxidation (FAO), providing additional energy and NADPH to sustain stemness characteristics, including sphere-forming capacity and tumor initiation. Mechanistically, OVOL2 not only suppressed STAT3 phosphorylation by directly inhibiting JAK transcription but also recruited histone deacetylase 1 (HDAC1) to STAT3, thereby reducing the transcriptional activation of downstream genes carnitine palmitoyltransferase1 (CPT1A and CPT1B)...

No abstract text is available yet for this article.

Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Crucially, research demonstrates that AI can analyse histopathology, radiology and natural language in liver cancer, and can replace manual tasks and access hidden information in routinely available clinical data. However, for liver cancer, few of these applications have translated into large-scale clinical trials or clinically approved products...

No abstract text is available yet for this article.

OBJECTIVE: To determine if reviewer experience impacts the ability to discriminate between human-written and ChatGPT-written abstracts. METHODS: Thirty reviewers (10 seniors, 10 juniors, and 10 residents) were asked to differentiate between 10 ChatGPT-written and 10 human-written (fabricated) abstracts. For the study, 10 gynecologic oncology abstracts were fabricated by the authors. For each human-written abstract we generated a ChatGPT matching abstract by using the same title and the fabricated results of each of the human generated abstracts...

BACKGROUND: Wearable sensors are increasingly being explored in healthcare, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. In particular, preprocessing pipelines to clean and standardize raw data have not been fully optimized. OBJECTIVE: The aim of this study was to conduct a systematic review of preprocessing techniques employed on wearable sensor data to ensure their readiness for artificial intelligence/machine learning ("AI/ML-ready") applications...

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions...

PURPOSE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa. MATERIAL AND METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features...

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017...

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity...