American Journal of Clinical Oncology-Cancer Clinical Trials最新文献

{"title":"Deep Learning-Based Risk Factor Analysis for Accurate Prediction of Lung Cancer in High-Risk Populations.","authors":"Anand Sharma, N M Kandoi","doi":"10.1097/COC.0000000000001284","DOIUrl":"10.1097/COC.0000000000001284","url":null,"abstract":"<p><strong>Objectives: </strong>Lung cancer remains a leading cause of cancer-related deaths worldwide, largely due to late diagnosis and the complexity of its risk factors. Early detection and accurate risk prediction are critical to improving patient survival and reducing treatment costs.</p><p><strong>Methods: </strong>This study presents a novel deep learning framework combining advanced techniques such as the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), Whale Optimization Algorithm with Adaptive Particle Swarm Optimization (WOA-APSO), convolutional neural networks (CNN), and Kernel-based non-Gaussian CNN (KNG-CNN) implemented in PYTHON to enhance lung cancer risk prediction.</p><p><strong>Results: </strong>The proposed model effectively optimizes feature selection and achieves a high prediction accuracy of 99.25%. These findings demonstrate the potential of integrating deep learning and optimization algorithms for precise risk stratification, facilitating early diagnosis, and personalized treatment.</p><p><strong>Conclusions: </strong>This work underscores the transformative impact of AI-driven approaches in lung cancer prognosis and highlights future opportunities for improving clinical outcomes.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":"413-423"},"PeriodicalIF":1.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate Lung Cancer Prediction From CT Scans Using Advanced Deep Learning Methods.","authors":"Anand Sharma, Narendra M Kandoi","doi":"10.1097/COC.0000000000001286","DOIUrl":"10.1097/COC.0000000000001286","url":null,"abstract":"<p><strong>Objectives: </strong>Accurate lung cancer prediction from CT scans using advanced deep learning methods is crucial for improving early diagnosis and treatment outcomes, as it harnesses innovative algorithms to enhance the detection and classification of malignant lesions in imaging data. The comprehensive approach for accurate lung cancer prediction from CT scans using advanced deep learning methods. Lung cancer remains one of the leading causes of cancer-related deaths globally, emphasizing the need for early and precise diagnosis.</p><p><strong>Methods: </strong>They propose a multistage framework that integrates state-of-the-art techniques, including hybrid Graph Convolutional Networks (GCNs) and Conditional Random Fields (CRFs) for image segmentation, followed by an innovative feature extraction pipeline utilizing Capsule Networks (CapsNets), Siamese Neural Networks, and Hybrid Deep Autoencoders. This combination allows for the effective identification of lung regions and the detection of potential lesions, ensuring high segmentation accuracy and robustness against noise.</p><p><strong>Results: </strong>The feature extraction implements a refined classification strategy that merges a Hybrid CNN-Transformer Model with Graph Neural Networks (GNNs). This dual approach leverages CNNs for capturing local patterns and transformers for modelling long-range dependencies, enhancing the ability to recognize subtle features indicative of malignancies. GNNs further contribute by modelling spatial and relational information among extracted features, facilitating a deeper understanding of the lung's complex anatomic structures.</p><p><strong>Conclusions: </strong>The proposed technique also leads with 91%, compared with LSTM's 80%, FNN's 70%, and RNN's 70%, highlighting its ability to minimize false positives, implemented using Python software. The future scope for accurate lung cancer prediction from CT scans using advanced deep learning methods includes the development of more sophisticated algorithms that integrate multimodal imaging data, enhancing diagnostic precision, and personalization of treatment plans.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":"424-438"},"PeriodicalIF":1.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surveillance Strategies in Follicular Non-Hodgkin's Lymphoma's Using Molecular and Genetic Markers Improve Cost-efficiencies Over Routine Imaging Studies.","authors":"Cherian Verghese, Kapil Meleveedu, Urja Nagadia, Niravkumar Brahmbhatt, Renju Raj, Arindam Bagchi, Wael Ghalayini, Nebu Koshy","doi":"10.1097/COC.0000000000001282","DOIUrl":"10.1097/COC.0000000000001282","url":null,"abstract":"<p><p>Follicular lymphoma is the most common indolent non-Hodgkin's lymphoma subtype. Newer therapies have improved outcomes leading to longer survival in 80% of follicular lymphomas. Only 20% patients have either early relapses or undergo transformation to more aggressive forms. Hence, using routine surveillance strategies for all follicular lymphomas is not likely to be cost-effective. Prior prognostication models, including IPI, FLIPI, and FLIPI 2 have limitations in predicting the behavior of these subtypes. Attempts have been therefore made to improve their ability to characterize follicular lymphomas that are likely to have adverse outcomes. These newer models improve upon clinical outcome indicators by adding molecular and genetic markers. These include PRIMA23, ICA13 (Independent Component Analysis 13), and POD24-PI (Progression of Disease Within 24 Months-Prognostic Index). These augmented prognostic models increase the sensitivity and specificity of predicting adverse pathologic behavior. In this paper we look at prevalence of these molecular and genetic markers before treatment and then looked for their presence in patients who experienced early relapses to define their utility in developing selective surveillance strategies. Over 10 molecular and genetic markers were uniquely present in patients who relapsed and these were also different from those who had disease transformation. These markers likely indicate clonal remnants that evade initial treatment causing early relapses. A selective surveillance strategy is therefore possible using molecular and genetic markers. This will help create a more cost-effective surveillance strategy in patients with follicular lymphomas.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":"452-457"},"PeriodicalIF":1.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13496027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Alterations in NSCLC: Prognostic Implications and Impact on Therapeutic Resistance.","authors":"Bhardwaj Tina Neelesh, Kanchan Bhardwaj, Phani Mn, Chirayu Padhiar","doi":"10.1097/COC.0000000000001289","DOIUrl":"10.1097/COC.0000000000001289","url":null,"abstract":"<p><p>Worldwide, the incidence of lung cancer is projected to continue its upward trend, with an estimated 2.5 million new cases annually. Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer, accounting for ~85% of all cases. One of the challenges associated with NSCLC management is incomplete understanding of the underlying molecular mechanisms. Tumors often harbor multiple genetic changes that interact in complex ways, influencing tumor behavior, including the growth rate, metastatic potential as well as response and resistance to therapies. Identification of genetic alterations is desirable to anticipate resistance mechanisms and guide the development of combination therapies to overcome them. It also allows better stratification of patients in clinical trials, ensuring that the therapies are tested in the most appropriate populations, improving the chances of identifying effective treatments and tailor treatment plans based on the specific genetic profile of a patient's tumor. This review summarizes the established genetic and epigenetic alterations associated with NSCLC and discusses the need for understanding the molecular pathogenesis.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":"458-464"},"PeriodicalIF":1.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Genomic Data and Imaging in Lung Cancer Prediction Using a Hybrid Deep Learning Approach.","authors":"Anand Sharma, Narendra M Kandoi","doi":"10.1097/COC.0000000000001288","DOIUrl":"10.1097/COC.0000000000001288","url":null,"abstract":"<p><strong>Objectives: </strong>Lung cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the urgent need for improved diagnostic and predictive methodologies. The several challenges in the complexity and high dimensionality of genomic data can lead to overfitting and computational inefficiencies, making it difficult to extract relevant features. The objective of this study is to develop a hybrid deep learning model that effectively integrates genomic data and imaging to enhance the accuracy of lung cancer prediction.</p><p><strong>Methods: </strong>The study utilizes the LIDC-IDRI data set for comprehensive data collection, focusing on both imaging and genomic data relevant to lung cancer prediction. In the data preprocessing phase, a LoGF is applied to refine the images, emphasizing edges and enhancing the detection of critical features, which supports more accurate predictions of lung cancer outcomes.</p><p><strong>Results: </strong>Imaging features are extracted from CT scans using various techniques, including texture analysis, shape descriptors, and deep learning-based methods, such as DCE imaging, which offers valuable insights into tumor vascularity and perfusion characteristics. The lung cancer prediction is conducted using hybrid deep learning techniques, employing the Inception-ResNet-v2 architecture, aimed at significantly enhancing diagnostic accuracy and facilitating early detection of lung cancer.</p><p><strong>Conclusions: </strong>The result shows that accuracy is the exactness of the models, with Inception-ResNet-v2 achieving the highest at 92.5%, implemented using Python software. Future research can explore the integration of additional multimodal data sources, such as electronic health records and lifestyle factors, to further enhance lung cancer prediction models.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":"439-451"},"PeriodicalIF":1.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Adjuvant Radiotherapy for the Prevention of Recurrent Hypertrophic Scars/Keloids: An American Radium Society (ARS) Appropriate Use Criteria (AUC).","authors":"Suzanne Russo, Corinne Wee, Samuel Jean-Baptiste, Austin N Kirschner, Bobby Koneru, Christien Kluwe, Diane C Ling, Neil Newman, Kevin Pearlstein, Yasamin Sharifzadeh, Rahul Tendulkar, Austin Dove","doi":"10.1097/COC.0000000000001366","DOIUrl":"https://doi.org/10.1097/COC.0000000000001366","url":null,"abstract":"<p><strong>Objectives: </strong>To summarize the evidence for adjuvant radiation therapy (RT) in the prevention of recurrent hypertrophic scars and keloids, including patient selection, optimal RT technique, and associated risks.</p><p><strong>Methods: </strong>This multi-specialty-led committee includes radiation oncology, plastic surgery, and dermatology physicians. Using the Population, Intervention, Comparator, Outcome, Timing, and Study Design (PICOTS) framework, evidence regarding treatment outcomes was assessed using Cochrane and PRISMA methodology. Eligible studies included prospective and retrospective studies published between January 1, 1980 and November 5, 2026, from the Ovid Medline database. Of the 165 articles identified using the search strategy, 87 were selected for inclusion as evidence, including 3 well-designed studies (phase II randomized and phase III), 23 moderately well-designed studies that account for most common biases (matched cohort and phase II studies), 50 studies with design limitations (retrospective reviews), 4 supplemental studies (not useful as primary evidence), and 7 meta-analyses. Eight additional studies were included to provide context and were not used to guide recommendations. Well-established RAND-UCLA consensus methodology (modified Delphi) was used to rate the appropriateness of the treatment options.</p><p><strong>Results: </strong>The results of this analysis demonstrate that RT is an effective and safe treatment strategy to reduce the risk of recurrent hypertrophic scars and keloids following surgery in appropriately selected patients.</p><p><strong>Conclusions: </strong>This systematic review provides consensus guidelines for the use of adjuvant RT for the prevention of recurrent hypertrophic scars/keloids based on the available peer-reviewed evidence in the literature.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data.","authors":"Reyanguli Dawuti, Lili Yao, Mayila Wutikuer, Xiaoshan Liu, Dongrui Zhang, Tingting Qi","doi":"10.1097/COC.0000000000001364","DOIUrl":"https://doi.org/10.1097/COC.0000000000001364","url":null,"abstract":"<p><strong>Objectives: </strong>Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers lack sufficient accuracy in prognostic prediction.</p><p><strong>Methods: </strong>In this population-based retrospective study, we analyzed 5392 patients with cervical squamous cell carcinoma from the surveillance, epidemiology, and end results (SEER) database between 2004 and 2015. Multivariable logistic regression and machine learning models were used to evaluate the associations between sociodemographic and clinical variables and overall survival.</p><p><strong>Results: </strong>The analysis identified that marital status, median household income, tumor grade (Grade Recode 2017), disease stage, and tumor size were collectively related to prognosis. Patients with lower socioeconomic status and residing in nonmetropolitan areas were found to have worse survival outcomes. A predictive model incorporating these variables, along with age and race, demonstrated acceptable discriminatory performance with an area under the ROC curve (AUC) of 0.70 in prognostic prediction.</p><p><strong>Conclusions: </strong>These findings suggested that both clinical and sociodemographic factors contributed meaningfully to prognosis in cervical squamous cell carcinoma, providing a basis for integrating these 2 types of factors in the development of stratified risk prediction tools that improve survival and quality of life.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Executive Summary of the American Radium Society (ARS) Appropriate Use Criteria (AUC) for Adjuvant Therapy for Resected Adenocarcinoma of the Pancreas.","authors":"Krishan R Jethwa, Neil B Newman, J Eva Selfridge, Christopher J Anker, Gerard Abood, Dmitriy Akselrod, Joseph Attallah, Christopher L Hallemeier, Leslie C Hassett, Zhaohui Jin, Ed Kim, Timothy Kennedy, Percy Lee, Diane C Ling, Eric D Miller, Navesh Sharma, William Small, Leila Tchelebi, Suzanne Russo","doi":"10.1097/COC.0000000000001347","DOIUrl":"https://doi.org/10.1097/COC.0000000000001347","url":null,"abstract":"<p><strong>Objectives: </strong>Rates of locoregional recurrence and distant metastases are high following curative-intent resection for patients with localized pancreatic adenocarcinoma. Adjuvant therapies have been investigated with the intent to reduce the risk of recurrence and improve OS. The objective of this study is to update and summarize the current evidence for adjuvant therapy following surgical resection for nonmetastatic PDAC with a focus on high-level evidence and randomized controlled trials is provided.</p><p><strong>Methods: </strong>This multispecialty-led committee included gastrointestinal radiation and medical oncology, gastroenterology, radiology, and surgical oncology. Using the population, intervention, comparator, outcome, timing and study design framework, evidence regarding treatment outcomes was assessed using Cochrane and PRISMA methodology. Eligible studies included prospective and retrospective (n≥25) studies published between January 1, 2013 and February 13, 2026, from Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane Database of Systematic Review, and Scopus databases. Study type and quality were assessed. Well-established RAND/UCLA consensus methodology (modified Delphi) was used to rate the appropriateness of the treatment options.</p><p><strong>Results: </strong>Of the 74 references used as evidence, 74 are categorized as therapeutic references. Study type and quality for the references were assessed, and included 31 well-designed studies (phase II randomized and phase III), 14 moderately well-designed studies that account for most common biases (matched cohort and phase II studies), 28 studies with design limitations (retrospective reviews), and 1 reference meta-analysis.</p><p><strong>Conclusions: </strong>This systematic review and guidelines provide up-to-date recommendations focusing on recent advancements for adjuvant treatment of resected PDAC using modern systemic therapy regimens and selective use of radiotherapy using modern techniques.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunotherapy in Colorectal Cancer: Novel Perspectives and Next Steps.","authors":"Thaís Sampaio Corrêa de Almeida, Maria Ignez de Melro Freitas Braghiroli, Jorge Sabbaga, Paulo Marcelo Gehm Hoff","doi":"10.1097/COC.0000000000001362","DOIUrl":"https://doi.org/10.1097/COC.0000000000001362","url":null,"abstract":"<p><p>Colorectal cancer (CRC) is the third most common malignant neoplasm worldwide and the second leading cause of cancer-related mortality. Currently, only about 15% of newly diagnosed CRC cases and 5% of metastatic cases are classified as being microsatellite instability-high (MSI-H) and can benefit significantly from immune checkpoint blockade, while the majority of patients are microsatellite stable (MSS) and typically show limited responses to this strategy. This review aims to describe recent advances and emerging perspectives regarding the use of novel checkpoint inhibitors for MSS CRC. We conducted a structured review and critical analysis of the most relevant evidence on immunotherapy in CRC. Particular focus was placed on response outcomes in MSS CRC populations treated with enhanced anti-CTLA-4 antibody botensilimab (BOT) and the anti-PD-1 antibody balstilimab (BAL). The combination of BOT+BAL has shown promising efficacy in heavily pretreated or refractory CRC, with objective response rates up to 20% and disease control rates around 60%. In the neoadjuvant setting, the BOT+BAL regimen has produced unprecedented pathologic complete response (pCR) rates for MSS CRC tumors, achieving a pCR rate of up to 40% and partial responses up to 71%. Treatment was associated with manageable toxicity and no surgical delays, resulting in downstaging levels that may spare surgery and/or adjuvant chemotherapy. Current evidence suggests a potential shift in the therapeutic landscape of CRC. Immunotherapy benefits may extend beyond MSI-H tumors, offering new possibilities for the broader population of patients with MSS CRC.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pirtobrutinib in Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma: A Systematic Review.","authors":"Faiza Fatima, Aeliya Mirza, Muhammad I Bhatti, Fatima Hussain, Zara Shahzad","doi":"10.1097/COC.0000000000001363","DOIUrl":"https://doi.org/10.1097/COC.0000000000001363","url":null,"abstract":"<p><strong>Objectives: </strong>Pirtobrutinib, a noncovalent Bruton tyrosine kinase inhibitor, was developed to overcome resistance mutations that limit covalent BTK inhibitors and to offer improved tolerability. Three phase III randomized trials (BRUIN CLL-321, CLL-313, and CLL-314) have evaluated pirtobrutinib against distinct comparators in different chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) populations, but no pooled evidence exists, as of now.</p><p><strong>Methods: </strong>Following PRISMA 2020 guidance for systematic reviews without meta-analysis, we searched Embase, Scopus, and PubMed from inception to July 2026 for double-arm randomized trials of pirtobrutinib in CLL/SLL. We extracted safety and efficacy data from the trials and synthesized it narratively.</p><p><strong>Results: </strong>Of 500 identified records, 3 trials met eligibility criteria. In relapsed/refractory patients previously treated with a covalent BTK inhibitor (CLL-321), pirtobrutinib reduced progression or death by 46% versus investigator's-choice therapy (HR: 0.54). In treatment-naive patients (CLL-313), pirtobrutinib reduced risk by 80% versus bendamustine-rituximab (HR: 0.20). In CLL-314, pirtobrutinib was noninferior to ibrutinib on objective response rate (87.0% vs. 78.5%) with a favorable early progression-free survival trend (HR: 0.57). Overall survival data remained inconclusive across trials, complicated by substantial crossover. Importantly, pirtobrutinib consistently showed lower rates of grade ≥3 adverse events, discontinuations, and cardiovascular toxicity, including markedly reduced atrial fibrillation versus ibrutinib.</p><p><strong>Conclusions: </strong>Pirtobrutinib demonstrates consistent efficacy and a favorable safety profile across relapsed/refractory and treatment-naive CLL/SLL settings, with the strongest evidence in patients who progressed on covalent BTK inhibitors. However, longer follow-up and head-to-head comparisons with other covalent inhibitors are warranted to establish its frontline role in this population.</p>","PeriodicalId":50812,"journal":{"name":"American Journal of Clinical Oncology-Cancer Clinical Trials","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}