{"title":"Conflict disrupts global supply chains for cancer medicines","authors":"Paul Adepoju","doi":"10.1016/s1470-2045(26)00138-5","DOIUrl":"https://doi.org/10.1016/s1470-2045(26)00138-5","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"197 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grant D Stewart, Sarah Boyce, Marie C Harrisingh, Olivia Crane
{"title":"NICE 2026 guideline for the diagnosis and management of kidney cancer","authors":"Grant D Stewart, Sarah Boyce, Marie C Harrisingh, Olivia Crane","doi":"10.1016/s1470-2045(26)00116-6","DOIUrl":"https://doi.org/10.1016/s1470-2045(26)00116-6","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"41st Annual European Association of Urology Congress 2026","authors":"Jamie Prowse","doi":"10.1016/s1470-2045(26)00140-3","DOIUrl":"https://doi.org/10.1016/s1470-2045(26)00140-3","url":null,"abstract":"<h2>Section snippets</h2><section><section><h2>Organised prostate cancer testing</h2>Improved risk stratification in prostate testing could ensure effective resource allocation, according to results from the OPT Stockholm3 study. In the population-based study, Ugo Falagario and colleagues (University of Foggia, Foggia, Italy) assessed whether reflex testing with Stockholm3 after PSA testing could reduce the proportion of men referred for MRI versus a PSA-only pathway. The OPT Stockholm3 pathway invited all men in the Region Stockholm Gotland in 2024 (born in 1974; Stockholm3</section></section><section><section><h2>Erda-iDRS</h2>Antoni Vilaseca and colleagues (Hospital Clínic de Barcelona, Barcelona, Spain) presented the final analysis of a phase 1, first-in-human study of an erdafitinib intravesical drug-releasing system (erda-iDRS) in patients with non-muscle-invasive bladder cancer (NMIBC) and select <em>FGFR</em> alterations. Cohort 1 included patients with recurrent, BCG-experienced high-risk NMIBC (high-grade Ta/T1, papillary only), who had refused or were ineligible for radical cystectomy, and with all visible tumours</section></section><section><section><h2>TULSA <em>vs</em> robotic prostatectomy</h2>Procedural outcomes and early functional recovery for CAPTAIN, a multicentre randomised controlled trial comparing MRI-guided transurethral ultrasound ablation (TULSA) with robotic prostatectomy in patients with intermediate-risk prostate cancer (primary localised, ISUP 2/3, and PSA ≤20 ng/mL), were presented by Laurence Klotz and colleagues (University of Toronto, Toronto, ON, Canada). 211 men from 23 sites across Europe, Canada, and the USA were randomly assigned to TULSA (n=148) or robotic</section></section><section><section><h2>CAPItello-281</h2>PTEN deficiency is associated with poor prognosis and reduced response to androgen receptor pathway inhibitors (ARPis) in patients with prostate cancer. Noel Clarke and colleagues (Christie and Salford Royal NHS Foundation Trusts, Manchester, UK) presented a preplanned exploratory analysis of the phase 3 CAPItello-281 trial. Patients with PTEN deficient metastatic hormone-sensitive prostate cancer were randomly assigned (1:1) to receive capivasertib or placebo plus prednisone or prednisolone</section></section><section><section><h2>Paradigm 1</h2>Neal Shore and colleagues (Carolina Urologic Research Center, Myrtle Beach, SC, USA) reported the first interim results from the phase 1 Paradigm 1 trial, assessing a novel microbial immunotherapeutic (ZH9) in patients with relapsed intermediate-risk or high-risk, BCG-unresponsive or BCG-naive NMIBC. 22 participants were enrolled (mean age 71 years; 77% with high-risk NMIBC) and 12 patients were dosed in four cohorts (from 1 × 10<sup>8</sup> CFU to 1 × 10<sup>11</sup> CFU). Ten patients were enrolled in the ongoing</section></section>","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emily Alger, Antoine Regnault, Amylou C Dueck, Madeline Pe, Michael J Grayling, Melanie J Calvert, Aaron R Hansen, Olga Kholmanskikh, Julia Lai-Kwon, J Jack Lee, Anna Minchom, Yu Qiao, Khadija Rerhou Rantell, Jessica Roydhouse, Claire Snyder, Stefan N Symeonides, Nolan A Wages, Roger Wilson, Christina Yap
{"title":"A practical toolkit with recommendations for analysing and visualising patient-reported outcomes in early phase dose-finding oncology trials (OPTIMISE-AR)","authors":"Emily Alger, Antoine Regnault, Amylou C Dueck, Madeline Pe, Michael J Grayling, Melanie J Calvert, Aaron R Hansen, Olga Kholmanskikh, Julia Lai-Kwon, J Jack Lee, Anna Minchom, Yu Qiao, Khadija Rerhou Rantell, Jessica Roydhouse, Claire Snyder, Stefan N Symeonides, Nolan A Wages, Roger Wilson, Christina Yap","doi":"10.1016/s1470-2045(26)00018-5","DOIUrl":"https://doi.org/10.1016/s1470-2045(26)00018-5","url":null,"abstract":"Patient-reported outcomes (PROs) are increasingly recognised for their role in assessing tolerability in dose-finding oncology trials (DFOTs). However, analysis and reporting of PRO data within DFOTs are often unclear and inconsistent. OPTIMISE-AR (Incorporating Patient-Reported Outcomes in Dose-Finding Trials–Analysis Recommendations) establishes a practical toolkit supporting the statistical analysis, visualisation, and reporting of PRO data within DFOT publications. International, multidisciplinary, cross-sector statistical analysis and data visualisation working groups identified analytical and visualisation approaches for PROs data, addressing key DFOT PRO research objectives. Informed by existing literature, case studies and recommendations are provided in this Policy Review for analysing binary, ordinal, and continuous PRO data to assess tolerability across doses and timepoints, and to integrate PROs into interim and final dose-decision processes. The OPTIMISE-AR toolkit is structured around four methodological domains aligned with key DFOT PRO research objectives, providing statistical analysis and data visualisation recommendations for (1) PRO endpoints across timepoints, (2) PRO endpoints between timepoints, (3) time-to-event PRO endpoints, and (4) PRO endpoints for formal dose-decision making in model-based dose-finding designs. As PROs have an increasing role in tolerability assessment, this Policy Review promotes analysis and data visualisation of PRO data, facilitating robust, patient-centred tolerability conclusions and supporting the broader development of tolerable and effective treatments.","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gil Shamai, Shachar Cohen, Yoav Binenbaum, Edmond Sabo, Alexandra Cretu, Chen Mayer, Iris Barshack, Tal Goldman, Gil Bar-Sela, António Polónia, Dezheng Huo, Alexander T Pearson, Frederick M Howard, Joseph A Sparano, Ron Kimmel, Dvir Aran
{"title":"Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study","authors":"Gil Shamai, Shachar Cohen, Yoav Binenbaum, Edmond Sabo, Alexandra Cretu, Chen Mayer, Iris Barshack, Tal Goldman, Gil Bar-Sela, António Polónia, Dezheng Huo, Alexander T Pearson, Frederick M Howard, Joseph A Sparano, Ron Kimmel, Dvir Aran","doi":"10.1016/s1470-2045(25)00727-2","DOIUrl":"https://doi.org/10.1016/s1470-2045(25)00727-2","url":null,"abstract":"<h3>Background</h3>Genomic assays such as Oncotype DX have transformed adjuvant treatment selection for hormone receptor-positive, HER2-negative, early breast cancer but remain inaccessible to many patients because of high cost and logistical barriers. We aimed to develop and validate an artificial intelligence (AI) model that estimates Oncotype DX 21-gene recurrence scores directly from routine histopathology slides and clinicopathological variables.<h3>Methods</h3>In this multicentre, model development and validation study, a multimodal deep-learning model was trained on digital whole-slide images and clinical features using a foundation model pre-trained on 171 189 histopathology slides for predicting Oncotype DX recurrence score. We included slides from patients with hormone receptor-positive, HER2-negative, invasive breast cancers and without scanning artifacts and with at least 100 tissue tiles (1·6 mm<sup>2</sup>). The model was fine-tuned and validated on the TAILORx randomised trial (8284 patients after quality control). Prognostic and predictive performance was assessed in the TAILORx-test set and externally validated in six independent cohorts (Carmel, Haemek, and Sheba medical centres [Israel], the University of Chicago Medical Center [USA], the Australian Breast Cancer Tissue Bank [Australia], and the Cancer Genome Atlas Breast Invasive Carcinoma project [USA]).<h3>Findings</h3>In the TAILORx-test set (n=2407), the AI model classified 1097 (45·6%) patients as low risk, 1021 (42·4%) as intermediate risk, and 289 (12·0%) as high risk. For identifying high genomic-risk disease (recurrence score ≥26), the area under the curve (AUC) was 0·898 (95% CI 0·879–0·913). AI-based risk stratification was prognostic for recurrence-free interval (hazard ratio 2·61 [95% CI 1·68–4·04]), distant recurrence-free interval (2·88 [1·73–4·79]), and disease-free survival (1·32 [0·92–1·89]). Chemotherapy benefit was evident in premenopausal patients classified by AI as being at high risk (0·63 [0·46–0·86]) but absent in postmenopausal patients classified by AI as being at low risk (0·94 [0·78–1·12]). 151 (31·3%) clinically high-risk postmenopausal women (by MINDACT criteria) were reclassified as low AI risk with no chemotherapy benefit. Analysis on external cohorts (5497 patients) showed that the model is transferable to new data with high generalisability (recurrence score ≥26 AUC ranging from 0·858 to 0·903).<h3>Interpretation</h3>These findings show that AI applied to routine histopathology can serve as a practical and scalable tool for guiding chemotherapy decisions in hormone receptor-positive, HER2-negative, early breast cancer. This approach has the potential to reduce unnecessary chemotherapy and broaden access to precision oncology, particularly in resource-limited settings where genomic testing remains unavailable or unaffordable.<h3>Funding</h3>Israel Innovation Authority (Kamin), Zimin Institute for Artificial Intelligence Solutions in Healthcare","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Benmelstobart plus anlotinib versus pembrolizumab as first-line treatment for PD-L1-positive, advanced non-small-cell lung cancer (CAMPASS): a blinded, randomised, controlled, phase 3 trial","authors":"Hua Zhong, Jing Wang, Runxiang Yang, Yongzhong Luo, Wei Zuo, Wei Zhang, Chao Xie, Qingshan Li, Qiang Liu, Xingxiang Xu, Qiming Wang, Yan Yu, Yongxing Chen, Tienan Yi, Xuhong Min, Jinsheng Shi, Jian Yang, Hongmei Sun, Hualin Chen, Huaqiu Shi, Baohui Han","doi":"10.1016/s1470-2045(26)00049-5","DOIUrl":"https://doi.org/10.1016/s1470-2045(26)00049-5","url":null,"abstract":"<h3>Background</h3>PD-1 and PD-L1 inhibitors have been shown to synergise with anti-angiogenic agents in non-small-cell lung cancer (NSCLC). We aimed to compare benmelstobart plus anlotinib with pembrolizumab in patients with previously untreated, driver gene-negative, PD-L1-positive, advanced NSCLC.<h3>Methods</h3>The blinded, randomised, controlled, phase 3 CAMPASS trial was conducted in 79 centres across China. Patients aged 18–75 years with stage IIIB–IV squamous or non-squamous NSCLC, no previous systemic treatment for advanced, recurrent or metastatic diseases, a PD-L1 tumour proportion score of 1% or greater, a life expectancy of 3 months or longer, at least one measurable lesion, and an Eastern Cooperative Oncology Group performance status of 0 or 1 were randomly assigned (2:1) to receive intravenous benmelstobart (1200 mg once on day 1) plus oral anlotinib (12 mg daily on days 1–14) or intravenous pembrolizumab (200 mg once on day 1) plus placebo every 3 weeks. Randomisation was done centrally and stratified by tumour histology, PD-L1 tumour proportion score, and brain metastases. Treatment allocation was open label for investigators and masked to patients and statisticians. The primary endpoint was progression-free survival as assessed by a blinded independent review committee per Response Evalutation Criteria in Solid Tumours version 1.1 in the intention-to-treat population (all randomly assigned patients). Safety was assessed in all randomly assigned patients who received at least dose of study drug. Results reported here are from a preplanned final analysis for progression-free survival. This ongoing study is closed to recruitment and is registered with <span><span>ClinicalTrials.gov</span><svg aria-label=\"Opens in new window\" focusable=\"false\" height=\"20\" viewbox=\"0 0 8 8\"><path d=\"M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z\"></path></svg></span>, <span><span>NCT04964479</span><svg aria-label=\"Opens in new window\" focusable=\"false\" height=\"20\" viewbox=\"0 0 8 8\"><path d=\"M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z\"></path></svg></span>.<h3>Findings</h3>Between Aug 6, 2021, and Dec 14, 2022, 531 patients were randomly assigned (354 to the benmelstobart plus anlotinib group and 177 to the pembrolizumab plus placebo group). 449 (85%) patients were male, 82 (15%) were female, and 493 (93%) were of Han ethnicity. Two patients in the benmelstobart plus anlotinib group and one patients in the pembrolizumab plus placebo group were untreated and therefore excluded from the safety population. After a median follow-up of 11·4 months (95% CI 9·4–13·1) for the benmelstobart plus anlotinib group and 10·6 months (9·0–13·0) for the pembrolizumab plus placebo group, median progression-free survival was 11·0 months (9·2–12·6) and 7·1 months (5·8–9·5), respectively (hazard ratio [HR] 0·70 [95% CI 0·54–0·90]; log-rank p=0·0057). Grade 3 or worse treatment-related a","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
