Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 4, 2025

Background Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications chemotherapy development, cancer diagnosis, and predicting response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) deep (DL). Objective This review aims to explore role forecasting outcomes related treatment response, synthesizing current advancements identifying critical gaps field. Methods A comprehensive literature search was conducted across PubMed, Embase, Web Science, Cochrane databases up 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” “Deep (DL)” combined with “chemotherapy development,” “cancer diagnosis,” treatment.” Articles published within last four years written English were included. The Prediction Model Risk Bias Assessment utilized assess risk bias selected studies. Conclusion underscores substantial impact AI, including ML DL, on innovation, response for both solid hematological tumors. Evidence from recent studies highlights AI’s potential reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing plans, improving therapeutic outcomes. Future research should focus addressing challenges clinical implementation, ethical considerations, scalability enhance integration into oncology care.

Language: Английский

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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review

Cancer Reports, Journal Year: 2025, Volume and Issue: 8(3)

Published: March 1, 2025

ABSTRACT Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall (OS), recurrence‐free (RFS), progression‐free (PFS), and treatment response prediction (TRP), are examined to evaluate effectiveness these identify significant features that influence predictive accuracy. Recent Findings A thorough search four major databases—PubMed, Scopus, Web Science, Cochrane—resulted 2400 articles published within last decade, with 32 studies meeting inclusion criteria. Notably, most publications emerged after 2021. Commonly used included random forest, support vector machines, logistic regression, XGBoost, various deep models. Evaluation metrics such as area under curve (AUC) (18 studies), concordance index (C‐index) (11 accuracy studies) were frequently employed. Age at diagnosis, tumor stage, CA‐125 levels, treatment‐related factors consistently highlighted predictors, emphasizing their relevance OC prognosis. Conclusion ML models demonstrate considerable potential outcomes; however, challenges persist regarding model interpretability. Incorporating diverse data types—such clinical, imaging, molecular datasets—holds promise enhancing capabilities. Future advancements will depend on integrating heterogeneous sources multimodal approaches, which crucial improving precision OC.

Language: Английский

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Personality traits of women with hereditary risk for breast/ovarian cancer versus obstetric history and cancer preventive behaviors

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 25, 2025

Abstract The aim of the study is to analyze relationship between personality traits women with hereditary predisposition breast/ovarian cancer and their obstetric history cancer-preventive behaviors. A total 357 women, participants ‘The National Program for Families With Genetic/Familial High Risk Cancer’, were included in study. Neo Five-Factor Inventory (NEO-FFI) a standardized original questionnaire designed purpose used. Breast ultrasound examination at younger age was associated Extraversion . Openness Experience linked lower number children, more frequent use hormonal contraceptives, first breast examination. Women higher Agreeableness scores less likely contraceptives underwent later life. Conscientiousness increased risk developing who used examinations earlier life, while those had breastfed children chose have mammogram Personality affect health-related behaviors should be taken into account when designing theoretical models as well interventions regarding health habits.

Language: Английский

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Early Diagnosis of Ovarian Cancer: A Comprehensive Review of the Advances, Challenges, and Future Directions

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 406 - 406

Published: Feb. 7, 2025

Ovarian cancer (OC), the seventh most common in women and lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases >200,000 deaths being reported annually. OC characterized by late-stage diagnosis, poor prognosis, 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances genomics proteomics, effective early-stage diagnostic tools population-wide screening strategies remain elusive, contributing high mortality rates. The complex pathogenesis of involves diverse histological subtypes genetic predispositions, including BRCA1/2 mutations; notably, considerable proportion have hereditary component. Current modalities, imaging techniques (transvaginal ultrasound, computed/positron emission tomography, magnetic resonance imaging) biomarkers (CA-125 human epididymis protein 4), varying degrees sensitivity specificity, limited efficacy detecting OC. Emerging technologies, such as liquid biopsy, multiomics, artificial intelligence (AI)-assisted diagnostics, may enhance early detection. Liquid biopsies using circulating tumor DNA microRNAs are popular minimally invasive tools. Integrated multiomics has advanced biomarker discovery. AI algorithms improved interpretation risk prediction. Novel methods organoids multiplex panels explored overcome current limitations. This review highlights critical need for continued research innovation reduce mortality, improve patient outcomes posits personalized medicine, integrated emerging targeted initiatives collaborative efforts, which address care access disparities promote cost-effective, scalable strategies, potential combat

Language: Английский

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Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: March 26, 2025

Introduction Ovarian Cancer (OC) is one of the leading causes cancer deaths among women. Despite recent advances in medical field, such as surgery, chemotherapy, and radiotherapy interventions, there are only marginal improvements diagnosis OC using clinical parameters, symptoms very non-specific at early stage. Owing to computational algorithms, ensemble machine learning, it now possible identify complex patterns parameters. However, these do not provide deeper insights into prediction diagnosis. Explainable artificial intelligence (XAI) models, LIME SHAP Kernels, can decision-making process thus increasing their applicability. Methods The main aim this study design a computer-aided diagnostic system that accurately classifies detects ovarian cancer. To achieve objective, three-stage model game-theoretic approach based on values were built evaluate visualize results, analyzing important features responsible for prediction. Results Discussion results demonstrate efficacy proposed with an accuracy 98.66%. model’s consistency advantages compared single classifiers. validated conventional statistical methods p -test Cohen’s d highlight method. further validate ranking features, we -values top five bottom features. AI-based method detection, diagnosis, prognosis multi-modal real-life data, which mimics move clinician demonstration high performance. strategy lead reliable, accurate, consistent AI solutions detection management higher patient experience outcomes low cost, morbidity, mortality. This be beneficial millions women living resource-constrained challenging economies.

Language: Английский

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Artificial Intelligence to predict cancer risk, are we there yet? a comprehensive review across cancer types

European Journal of Cancer, Journal Year: 2025, Volume and Issue: unknown, P. 115440 - 115440

Published: April 1, 2025

Language: Английский

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Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: June 27, 2024

Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival of this type, early prediction disease has crucial role in decreasing improving prognosis. So, study.

Language: Английский

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New Trends in Ovarian Cancer Diagnosis Using Deep Learning: A Systematic Review

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 116587 - 116608

Published: Jan. 1, 2024

Ovarian cancer (OC) is one of the most common types in women. Surgery and chemotherapy are still forms treatment; however, their success depends on lots factors describing type cancer, size, shape, its origin, thus early accurate detection could bring benefits to increasing survival rate by applying custom/ personalized effective treatment. This why many researchers aim obtain computer-aided diagnosis (CAD) systems assist such diseases. The current paper presents a systematic review new trends designing different deep learning-based intelligent for OC diagnosis. advantages using learning approaches diagnosis, used methods, datasets. It performs detailed analysis concerning preferred, effective, architectures. Several 70 articles published journals impact conferences were investigated between 2018 2024, focusing 2021 2024. All included studies indexed PubMed, Scopus, or ISI Web Of Science.

Language: Английский

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Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis

Published: Aug. 8, 2024

Language: Английский

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Primary Cytoreductive Surgery Versus Neoadjuvant Chemotherapy Followed by Surgery in Patients with Advanced Primary Epithelial Ovarian Cancer in Low Resources Setting: A Randomized Clinical Trial

The Journal of Obstetrics and Gynecology of India, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

Language: Английский

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