Clinical oncology最新文献

{"title":"RCR meetings","authors":"","doi":"10.1016/S0936-6555(25)00088-3","DOIUrl":"10.1016/S0936-6555(25)00088-3","url":null,"abstract":"","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"40 ","pages":"Article 103833"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OncoFlash - Research Updates in a Flash!","authors":"A. Turcas , K. Thippu Jayaprakash","doi":"10.1016/j.clon.2025.103803","DOIUrl":"10.1016/j.clon.2025.103803","url":null,"abstract":"","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"40 ","pages":"Article 103803"},"PeriodicalIF":3.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising HER2-positive Breast Cancer Treatment: Insights on Subcutaneous Pertuzumab-trastuzumab Transition","authors":"S. Surekha , A.K. Lamiyan , P. Khatri , A.N. Patil","doi":"10.1016/j.clon.2025.103802","DOIUrl":"10.1016/j.clon.2025.103802","url":null,"abstract":"","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103802"},"PeriodicalIF":3.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Health Care: A Rallying Cry for Critical Clinical Research and Ethical Thinking","authors":"S.M. Bentzen","doi":"10.1016/j.clon.2025.103798","DOIUrl":"10.1016/j.clon.2025.103798","url":null,"abstract":"<div><div>Artificial intelligence (AI) will impact a large proportion of jobs in the short to medium term, especially in the developed countries. The consequences will be felt across many sectors including health care, a critical sector for implementation of AI tools because glitches in algorithms or biases in training datasets may lead to suboptimal treatment that may negatively affect the health of an individual. The stakes are obviously higher in case of potentially life-threatening diseases such as cancer and therapies with a potential for causing severe or even fatal adverse events.</div><div>Over the last two decades, much of the research on AI in health care has focussed on diagnostic radiology and digital pathology, but a solid body of research is emerging on AI tools in the radiation oncology workflow. Many of these applications are relatively uncontroversial, although there is still a lack of evidence regarding effectiveness rather than efficiency, and—the ultimate bar—evidence of clinical utility. Proponents of AI will argue that these algorithms should be implemented with robust human supervision. One challenge here is the deskilling effect associated with new technologies. We will become increasingly dependent on the AI tools over time, and we will become less capable of assessing the quality of the AI output.</div><div>Much of this research appears almost old-fashioned in view of the rapid advances in Generative artificial intelligence (GenAI). GenAI can draw from multiple types of data and produce output that is personalised and appears relevant in the given context. Especially the rapid progress in large language models (LLMs) has opened a wide field of potential applications that were out of bounds just a few years ago. One LLM, Generative Pre-trained Transformer 4 (GPT-4), has been made widely accessible to end-users as ChatGPT-4, which passed a rigorous Turing test in a recent study. In this viewpoint, I argue for the necessity of independent academic research to establish evidence-based applications of AI in medicine. Algorithmic medicine is an intervention similar to a new drug or a new medical device. We should be especially concerned about under-represented minorities and rare/atypical clinical cases that may drown in the petabyte-sized training sets. A huge educational push is needed to ensure that the end-users of AI in health care understand the strengths and weaknesses of algorithmic medicine. Finally, we need to address the ethical boundaries for where and when GenAI can replace humans in the relation between patients and healthcare providers.</div></div>","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103798"},"PeriodicalIF":3.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sexual Dysfunction in Prostate Cancer Patients According to Disease Stage and Treatment Modality","authors":"W. Kinnaird ,&nbsp;P. Schartau ,&nbsp;M. Kirby ,&nbsp;V. Jenkins ,&nbsp;S. Allen ,&nbsp;H. Payne","doi":"10.1016/j.clon.2025.103801","DOIUrl":"10.1016/j.clon.2025.103801","url":null,"abstract":"<div><h3>Aims</h3><div>To investigate physical and psychological sexual dysfunction (SD) in prostate cancer (PCa) patients, according to disease stage and treatment modality.</div></div><div><h3>Materials and methods</h3><div>Participants diagnosed with PCa completed an online survey reporting sexual side effects across 13 domains, the importance of sexual function, and their support needs. Disease stage and treatment data were collected to identify variations in experience. Results were analysed descriptively and with chi-squared significance testing.</div></div><div><h3>Results</h3><div>Six hundred fifty-four participants diagnosed with localised (66.1%), locally advanced (25.1%), and advanced (8.9%) PCa responded to the survey. Their disease management included radical prostatectomy (RP; 49.7%), radiotherapy (RT; 45.9%), and androgen deprivation therapy (ADT; 43.6%). More than 98% reported new-onset post-treatment sexual problems. The most common physical dysfunctions were erectile dysfunction (ED; 91.0%), ejaculatory disturbance (82.9%), and anatomical penile change (70.0%). The most common psychosexual dysfunctions were loss of sexual confidence (76.2%), loss of sex drive (67.1%), and loss of self-esteem (57.1%). Participants diagnosed with advanced disease were significantly more likely to report SD than participants with localised or locally advanced disease in 5 of 13 domains (p &lt; .05). Participants whose treatment included a combination of RP, RT, and ADT were most likely to report SD in 7 of 13 domains. Overall, 78.3% of participants said sexual activity was important to them, with 61.8% placing sexual problems in their top three current concerns. Furthermore, 78.3% wanted to discuss sexual problems with a healthcare professional, with most wishing to focus on ED, loss of sexual confidence, and low libido.</div></div><div><h3>Conclusion</h3><div>SD is a common, wide-ranging, and distressing side effect of treatment, and PCa survivors place a high level of importance on sexual recovery. Those with advanced disease are among the worst affected and report high levels of psychosexual problems. Holistic rehabilitation strategies addressing a broad range of side effects would benefit all, but particularly those treated with permanent ADT.</div></div>","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103801"},"PeriodicalIF":3.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Role of Pre-ablation Stimulated Thyroglobulin and Thyroid-Stimulating Hormone Ratio for Radioactive Iodine Treatment in Adults with Papillary Thyroid Cancer","authors":"X. Zhou ,&nbsp;X. Wang ,&nbsp;W. Song ,&nbsp;X. Yue ,&nbsp;Y. Li ,&nbsp;Y. Shi","doi":"10.1016/j.clon.2025.103797","DOIUrl":"10.1016/j.clon.2025.103797","url":null,"abstract":"<div><h3>Objective</h3><div>This investigation assesses the predictive utility of the pre-ablation stimulated thyroglobulin to thyroid-stimulating hormone ratio (sTg/TSH) and examines the other factors affecting the efficacy of radioactive iodine (RAI) therapy in adult patients with papillary thyroid cancer (PTC).</div></div><div><h3>Methods</h3><div>We performed a retrospective review of clinical and pathological data from 1071 patients who received a total thyroidectomy followed by RAI therapy. The study included 576 of these patients. Participants were separated into two groups according to their reaction to RAI therapy: excellent response (ER) and non-ER (NER). The factors that contribute to NER were found using univariate and multivariate binary logistic regression analyses. The predictive importance of the sTg and sTg/TSH ratio was discovered by analyzing receiver operating characteristic (ROC) curves and setting diagnostic criteria. Decision curve analysis (DCA) was used to assess the practical implications of these findings.</div></div><div><h3>Results</h3><div>Among the 576 patients assessed, 60.07% (346 individuals) demonstrated an ER to RAI treatment. Independent predictors of a NER identified through both univariate and multivariate logistic regression analyses included multifocality (odds ratio [OR] = 2.16, 95% confidence interval [CI]: 1.28–3.67, P = 0.004), having more than ten positive lymph nodes (PLN) (OR = 3.78, 95% CI: 1.68–8.54, P = 0.001), presence of distant metastases (OR = 19.22, 95% CI: 2.09–176.93, P = 0.009), elevated stimulated thyroglobulin (sTg) levels (OR = 1.04, 95% CI: 1.00–1.07, P = 0.025), and a higher sTg/TSH ratio (OR = 2.48, 95% CI: 1.80–3.41, P &lt; 0.001). Receiver operating characteristic (ROC) curve analysis established diagnostic thresholds for predicting NER at an sTg level of 7.255 ng/ml (area under the curve [AUC] = 0.893) and an sTg/TSH ratio of 0.127 (AUC = 0.889), both demonstrating robust sensitivity and specificity. Smooth curve fitting illustrated a progressive increase in the risk of NER with rising levels of the sTg/TSH ratio. DCA confirmed the substantial clinical net benefit of these predictors in forecasting NER outcomes.</div></div><div><h3>Conclusions</h3><div>The sTg/TSH ratio is confirmed as a reliable diagnostic marker for predicting the response to primary RAI treatment in PTC. Moreover, active postoperative follow-up and surveillance are essential for patients with multifocality, PLN &gt;10, sTg &gt;7.255 ng/ml, and sTg/TSH ratio &gt;0.127.</div></div>","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103797"},"PeriodicalIF":3.2,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset","authors":"D. Sharma ,&nbsp;G. Singh ,&nbsp;N. Burela ,&nbsp;S. Gayen ,&nbsp;G. Aishwarya ,&nbsp;S. Nangia","doi":"10.1016/j.clon.2025.103796","DOIUrl":"10.1016/j.clon.2025.103796","url":null,"abstract":"<div><h3><em>Aims</em></h3><div>To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).</div></div><div><h3><em>Materials and methods</em></h3><div>A 3D U-Net-based DLS model was applied to CT datasets of 124 HNC patients treated with IMPT at 50.4–70.0 GyRBE. Thirty organs-at-risk (OARs), delineated manually (GT-OARs) were analysed for similarity metrics with auto-segmented OARs, without (DLS-nonedited) and with (DLS-edited) manual correction, using volume, Dice similarity coefficient (DSC), and Hausdorff distance (HD). Dosimetric impact of auto-segmentation error was assessed as absolute dose difference of mean (ΔDmean) and maximum (ΔDmax).</div></div><div><h3><em>Results</em></h3><div>The cohort includes patients with postoperative (47.6%), flap reconstruction (12.1%), mouth bites (79.8%), dental implants (54.8%), and surgical implants (3.2%). DLS failed in 11 patients with significant anatomical challenges and artifact. Compared with GT-OARs, DLS-nonedited under-segmented 11/12 Gr-A (central nervous system, arteries, bone) (p &lt; 0.05) and over-segmented 13/18 Gr-B (glandular, digestive, airways) OARs. DSC scores were good (&gt;0.8), intermediate (0.6–0.8), intermediate–poor (0.5–0.6), and poor (&lt;0.5) in 12, 6, 4, and 8 OARs. HD were good (&lt;4mm), intermediate (4–6mm), poor (6–8mm), and very poor (&gt;8mm) in 5, 7, 4, and 14 OARs. Compared with manually corrected, DLS-edited OARs, all DLS-nonedited OARs demonstrated excellent similarity with DSC&gt;0.8 and HD&lt;4mm. On average, auto-segmentation took 2.51 minutes, while correction took 6.24 minutes. The mean values of ΔDmean and ΔDmax were within ±300 and ±3 cGyRBE, except for oesophagus and larynx, where the mean ΔDmean increases up to 837.14 cGyRBE.</div></div><div><h3><em>Conclusion</em></h3><div>Patient posture, nonbiological materials, and anatomical deformities influence DLS accuracy. The model’s overall performance is adequate and efficient with skilled manual editing needed for few OARs.</div></div>","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103796"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neoadjuvant Chemotherapy and Low Dose Immunotherapy in Resectable Non-small Cell Lung Cancer: A Multi-center Retrospective Cohort Analysis","authors":"M. Shah ,&nbsp;V. Noronha ,&nbsp;S. Rajamanickam Kulandaivel ,&nbsp;B. Poladia ,&nbsp;D. Niyogi ,&nbsp;N. Menon ,&nbsp;R. Kaushal ,&nbsp;O. Shetty ,&nbsp;T. Pai ,&nbsp;A. Tibdewal ,&nbsp;M. Vora ,&nbsp;D. Shah ,&nbsp;D. Vora ,&nbsp;S. Shah ,&nbsp;S. Goud ,&nbsp;A. Shah ,&nbsp;K. Maske ,&nbsp;A. Shetake ,&nbsp;K. Prabhash","doi":"10.1016/j.clon.2025.103795","DOIUrl":"10.1016/j.clon.2025.103795","url":null,"abstract":"","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103795"},"PeriodicalIF":3.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management of Head and Neck Squamous Cell Carcinoma With N3 Nodal Disease","authors":"L. Wang ,&nbsp;M. Ingle ,&nbsp;L. Oo ,&nbsp;A. Bains ,&nbsp;F. Lam ,&nbsp;A. James ,&nbsp;C. Podesta ,&nbsp;J. Virk ,&nbsp;Z. Awad ,&nbsp;D. Gujral","doi":"10.1016/j.clon.2025.103794","DOIUrl":"10.1016/j.clon.2025.103794","url":null,"abstract":"<div><h3>Aims</h3><div>Radical management of the N3 neck for head and neck squamous cell cancer (HNSCC) remains unclear. We aimed to investigate the use of primary surgery including neck dissection versus primary radiotherapy followed by imaging.</div></div><div><h3>Materials and methods</h3><div>We retrospectively reviewed consecutive patients with HNSCC and N3 nodal disease, excluding nasopharyngeal primaries. Patients had either surgical management of the primary and neck dissection followed by postoperative radiotherapy or primary radiotherapy followed by surveillance if complete response was found on post-treatment imaging. Patients were imaged at a mean of 16 weeks post radiotherapy. Patients identified with presence of resectable residual disease on imaging were treated with neck dissection.</div></div><div><h3>Results</h3><div>Between July 2012 and February 2023, 53 patients with T0-4N3M0 HNSCC were treated radically. The median (range) follow-up was 25.5 (3-146) months, with an opportunity for follow-up of 64 (19-147) months. Twenty-two patients had primary surgical management and 31 had primary radiotherapy. Two-year overall survival was 64% in patients treated with primary surgery, 55% in patients treated with primary radiotherapy, 87% in patients with complete response after radiotherapy, and 92% in complete responders who were p16 positive. Response assessment was done with positron emission tomography-computed tomography (PET-CT) in 77% of patients and predicted subsequent disease-free survival better than computed tomography (CT). p16-positive patients were more likely to achieve complete response (63% vs 25%), but extracapsular spread was not predictive of response.</div></div><div><h3>Conclusion</h3><div>Surveillance for patients with complete response on postradiotherapy PET-CT is a reasonable approach, especially for p16-positive patients, sparing them the morbidity of neck dissection. Patients with p16-negative disease are less likely to achieve a complete response and may be better managed with primary neck dissection.</div></div>","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"41 ","pages":"Article 103794"},"PeriodicalIF":3.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RCR meetings","authors":"","doi":"10.1016/S0936-6555(25)00045-7","DOIUrl":"10.1016/S0936-6555(25)00045-7","url":null,"abstract":"","PeriodicalId":10403,"journal":{"name":"Clinical oncology","volume":"39 ","pages":"Article 103790"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}