Journal of Cancer Research and Clinical Oncology最新文献

{"title":"Mitochondrial disruption resulting from Cepharanthine-mediated TOM inhibition triggers ferroptosis in colorectal cancer cells.","authors":"Liu-Gen Li, Di Zhang, Qi Huang, Min Yan, Nan-Nan Chen, Yan Yang, Rong-Cheng Xiao, Hui Liu, Ning Han, Abdul Moiz Qureshi, Jun Hu, Fan Leng, Yuan-Jian Hui","doi":"10.1007/s00432-024-05974-1","DOIUrl":"10.1007/s00432-024-05974-1","url":null,"abstract":"<p><strong>Background: </strong>Chemotherapy for colorectal cancer (CRC) urgently needs low-toxicity and highly effective phytomedicine. Cepharanthine (Cep) shown to have multiple anti-tumor effects, including colorectal cancer, whose pivotal mechanisms are not fully understood. Herein, the present work aims to reveal the impact of Cep on the mitochondrial and anti-injury functions of CRC cells.</p><p><strong>Methods: </strong>The TOM70/20 expression was screened by bioinformatic databases. SW480 cells were utilized as the colorectal cancer cell model. The expression of TOM70/20 and the downstream molecules were measured by western blots (WB). The ferroptosis was analyzed using Transmission electron microscopy (TEM), C11-BODIPY, PGSK, and DCFH-DA probes, wherein the detection was performed by flow cytometry and laser confocal microscopy. The anti-cancer efficacy was conducted by CCK-8 and Annexin-V/PI assay. The rescue experiments were carried out using Fer-1 and TOM70 plasmid transfection.</p><p><strong>Results: </strong>Bioinformatic data identified TOM20 and TOM70 were highly expressed in colorectal cancer, which could be down-regulated by Cep. Further findings disclosed that Cep treatment destroyed the mitochondria and inactivated the NRF2 signaling pathway, an essential pathway for resistance to ferroptosis, thereby promoting reactive oxygen species (ROS) generation in CRC cells. As a result, prominent ferroptosis could be observed in CRC cells in response to Cep, which thereby led to the reduced cell viability of cancer cells. On the contrary, recovery of TOM70 dampened the Cep-elicited mitochondria damage, ferroptosis, and anti-cancer efficacy.</p><p><strong>Conclusion: </strong>In summary, Cep-mediated TOM inhibition inactivates the NRF2 signaling pathway, thereby triggering ferroptosis and achieving an anti-colorectal cancer effect. The current study provides an innovative chemotherapeutic approach for colorectal cancer with phytomedicine.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11478973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466194","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":"SCYL1-mediated regulation of the mTORC1 signaling pathway inhibits autophagy and promotes gastric cancer metastasis.","authors":"Zihao Zhao, Jinlong Liu, Xian Gao, Zhuzheng Chen, Yilin Hu, Junjie Chen, Weijie Zang, Wanjiang Xue","doi":"10.1007/s00432-024-05938-5","DOIUrl":"10.1007/s00432-024-05938-5","url":null,"abstract":"<p><strong>Background: </strong>The SCY1-like (SCYL) family has been reported to be closely related to cancer metastasis, but it has not been reported in gastric cancer (GC), and its specific mechanism is not clear.</p><p><strong>Methods: </strong>We utilized databases like Deepmap, TCGA, and GEO to identify SCYL1's role in GC. Clinical samples were analyzed for SCYL1 expression and its correlation with patient prognosis. In vitro and in vivo experiments were conducted to assess SCYL1's function in GC cell migration, invasion, and autophagy.</p><p><strong>Results: </strong>SCYL1 showed an increased expression in GC tissues, which correlated with a negative prognosis. In vitro experiments demonstrated that SCYL1 promotes GC cell migration and invasion and inhibits autophagy. GSEA indicated an inverse relationship between SCYL1 and autophagy, while a direct relationship was observed with the mTORC1 signaling pathway. Knockdown of SCYL1 enhanced autophagy, while activation of mTORC1 reversed this effect.</p><p><strong>Conclusions: </strong>SCYL1 is a significant contributor to GC progression, promoting metastasis by activating the mTORC1 signaling pathway and inhibiting autophagy. These findings suggest SCYL1 as a potential therapeutic target for GC treatment.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11469978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406421","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":"RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.","authors":"Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz, Mohd Asif Shah","doi":"10.1007/s00432-024-05968-z","DOIUrl":"10.1007/s00432-024-05968-z","url":null,"abstract":"<p><strong>Problem: </strong>Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.</p><p><strong>Aim: </strong>This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity.</p><p><strong>Methods: </strong>We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.</p><p><strong>Results: </strong>The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.</p><p><strong>Conclusion: </strong>Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400360","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":"Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer.","authors":"Liping Yang, Mengyue Li, Yixin Liu, Zhiyun Jiang, Shichuan Xu, Hongchao Ding, Xing Gao, Shilong Liu, Lishuang Qi, Kezheng Wang","doi":"10.1007/s00432-024-05971-4","DOIUrl":"10.1007/s00432-024-05971-4","url":null,"abstract":"<p><strong>Background: </strong>Presently, the options of concurrent chemo-radiotherapy (CCR) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) are controversial and there is no reliable prediction tool to stratify poor- and good-responders. Although radiomic analysis has provided new opportunities for personalized medicine in oncological practice, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This study aimed to develop a qualitative radiomic signature based on the within-sample rank of radiomics features, and to use this novel method to predict CCR sensitivity in LA-NSCLC, avoiding the variability of quantitative signatures to multicenter effect.</p><p><strong>Methods: </strong>We retrospectively analyzed 125 patients with stage III NSCLC who received treatment from our hospital. Radiomic features were extracted from pretreatment plain CT scans and constructed as feature pairs based on their within-sample rank. Fisher and univariate Cox analyses were performed to select feature pairs significantly associated with patients' overall survival (OS). NSCLC-Radiomic (R422) cohort including 104 NSCLC patients was used as an independent testing cohort. NSCLC-Radiogenomic (RG211) cohort with matched RNA sequencing profiles, was used for functional enrichment analysis to reveal the underlying biological mechanism reflected by the signature.</p><p><strong>Results: </strong>A qualitative signature, consisting of 15 radiomic feature pairs (termed as 15-R_iFPS), was developed based on the Genetic Algorithm, which could optimally distinguish responder from non-responder with significantly improved OS if they received CCR treatment (log-rank P = 0.0009, HR = 13.79, 95% CIs 1.83-104.1). The performance of 15-R_iFPS was validated in an independent public cohort (log-rank P = 0.0037, HR = 2.40, 95% CIs 1.30-4.40). Furthermore, the transcriptomic analyses provided biological pathways ('glutathione metabolic process', 'cellular oxidant detoxification') underlying the signature.</p><p><strong>Conclusions: </strong>We developed a CT-derived 15-R_iFPS, which could potentially help predict individualized therapeutic benefit of CCR in patients with LA-NSCLC. Additionally, we investigated the underlying intra-tumoral biological characteristics behind 15-R_iFPS which would accelerate its clinical application. This approach could be applied to a wider range of treatments and cancer types.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466187","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":"Quality of life under treatment with the immune checkpoint inhibitors ipilimumab and nivolumab in melanoma patients. Real-world data from a prospective observational study at the Skin Cancer Center Kiel.","authors":"Carolin Grote, Ann-Sophie Bohne, Christine Blome, Katharina C Kähler","doi":"10.1007/s00432-024-05981-2","DOIUrl":"10.1007/s00432-024-05981-2","url":null,"abstract":"<p><strong>Purpose: </strong>Combined immunotherapy (ipilimumab + nivolumab) has improved survival in stage IV melanoma patients, making Health-related Quality of Life (HrQoL) crucial due to potential immune-related adverse events (irAEs). Previous studies treated HrQoL as secondary/explorative endpoint, and no specific HrQoL questionnaire for melanoma patients on immune checkpoint inhibitor (ICI) therapy exists. This study aimed to gather specific HrQoL data during combined ICI therapy, tracking changes during and after treatment, and examining associations with gender, irAEs, and treatment response.</p><p><strong>Methods: </strong>35 melanoma patients (22 males, 13 females) undergoing combined ICI were surveyed using the Short-form 36 questionnaire (SF-36), the Inflammatory Bowel Disease Questionnaire - Deutsch (IBDQ-D), and the distress thermometer (DT). HrQoL was evaluated during treatment, after six months, and at the onset of autoimmune colitis.</p><p><strong>Results: </strong>irAEs occurred in 51.4% of patients, with colitis being the most common (26.1%). 45.7% had progressive disease. SF-36 showed stable HrQoL during treatment and follow-up. Women had worse HrQoL on the physical component scale than men (p = 0.019). Patients with progression showed worse HrQoL over time in physical (p = 0.015) and mental health scales (p = 0.04). IBDQ-D showed constant HrQoL throughout treatment and follow-up. Distress on DT remained constant, with women reporting higher levels of distress.</p><p><strong>Conclusion: </strong>HrQoL remained stable during and after therapy. Female gender and disease progression negatively impacted HrQoL. The development of irAEs was not associated with HrQoL, though this may not apply to severe irAEs like colitis, which were not assessed.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400359","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":"Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.","authors":"Jianxin Chen, Fengyi Lin, Zhaoyan Dai, Yu Chen, Yawen Fan, Ang Li, Chenyu Zhao","doi":"10.1007/s00432-024-05905-0","DOIUrl":"10.1007/s00432-024-05905-0","url":null,"abstract":"<p><strong>Purpose: </strong>We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature).</p><p><strong>Methods: </strong>369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC).</p><p><strong>Results: </strong>A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort.</p><p><strong>Conclusions: </strong>DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390799","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":"Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study.","authors":"Yunjun Yang, Zhenyu Xu, Zhiping Cai, Hai Zhao, Cuiling Zhu, Julu Hong, Ruiliang Lu, Xiaoyu Lai, Li Guo, Qiugen Hu, Zhifeng Xu","doi":"10.1007/s00432-024-05986-x","DOIUrl":"10.1007/s00432-024-05986-x","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis.</p><p><strong>Results: </strong>The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model.</p><p><strong>Conclusion: </strong>The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390797","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":"Proof-of-concept study of a small language model chatbot for breast cancer decision support - a transparent, source-controlled, explainable and data-secure approach.","authors":"Sebastian Griewing, Fabian Lechner, Niklas Gremke, Stefan Lukac, Wolfgang Janni, Markus Wallwiener, Uwe Wagner, Martin Hirsch, Sebastian Kuhn","doi":"10.1007/s00432-024-05964-3","DOIUrl":"10.1007/s00432-024-05964-3","url":null,"abstract":"<p><strong>Purpose: </strong>Large language models (LLM) show potential for decision support in breast cancer care. Their use in clinical care is currently prohibited by lack of control over sources used for decision-making, explainability of the decision-making process and health data security issues. Recent development of Small Language Models (SLM) is discussed to address these challenges. This preclinical proof-of-concept study tailors an open-source SLM to the German breast cancer guideline (BC-SLM) to evaluate initial clinical accuracy and technical functionality in a preclinical simulation.</p><p><strong>Methods: </strong>A multidisciplinary tumor board (MTB) is used as the gold-standard to assess the initial clinical accuracy in terms of concordance of the BC-SLM with MTB and comparing it to two publicly available LLM, ChatGPT3.5 and 4. The study includes 20 fictional patient profiles and recommendations for 5 treatment modalities, resulting in 100 binary treatment recommendations (recommended or not recommended). Statistical evaluation includes concordance with MTB in % including Cohen's Kappa statistic (κ). Technical functionality is assessed qualitatively in terms of local hosting, adherence to the guideline and information retrieval.</p><p><strong>Results: </strong>The overall concordance amounts to 86% for BC-SLM (κ = 0.721, p < 0.001), 90% for ChatGPT4 (κ = 0.820, p < 0.001) and 83% for ChatGPT3.5 (κ = 0.661, p < 0.001). Specific concordance for each treatment modality ranges from 65 to 100% for BC-SLM, 85-100% for ChatGPT4, and 55-95% for ChatGPT3.5. The BC-SLM is locally functional, adheres to the standards of the German breast cancer guideline and provides referenced sections for its decision-making.</p><p><strong>Conclusion: </strong>The tailored BC-SLM shows initial clinical accuracy and technical functionality, with concordance to the MTB that is comparable to publicly-available LLMs like ChatGPT4 and 3.5. This serves as a proof-of-concept for adapting a SLM to an oncological disease and its guideline to address prevailing issues with LLM by ensuring decision transparency, explainability, source control, and data security, which represents a necessary step towards clinical validation and safe use of language models in clinical oncology.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390798","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":"Deep learning model using planar whole-body bone scintigraphy for diagnosis of skull base invasion in patients with nasopharyngeal carcinoma.","authors":"Xingyu Mu, Zhao Ge, Denglu Lu, Ting Li, Lijuan Liu, Cheng Chen, Shulin Song, Wei Fu, Guanqiao Jin","doi":"10.1007/s00432-024-05969-y","DOIUrl":"10.1007/s00432-024-05969-y","url":null,"abstract":"<p><strong>Purpose: </strong>This study assesses the reliability of deep learning models based on planar whole-body bone scintigraphy for diagnosing Skull base invasion (SBI) in nasopharyngeal carcinoma (NPC) patients.</p><p><strong>Methods: </strong>In this multicenter study, a deep learning model was developed using data from one center with a 7:3 allocation to training and internal test sets, to diagnose SBI in patients newly diagnosed with NPC using planar whole-body bone scintigraphy. Patients were diagnosed based on a composite reference standard incorporating radiologic and follow-up data. Ten different convolutional neural network (CNN) models were applied to both whole-image and partial-image input modes to determine the optimal model for each analysis. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis (DCA), and compared with expert assessments by two nuclear medicine physicians.</p><p><strong>Results: </strong>The best-performing model using partial-body input achieved AUCs of 0.80 (95% CI: 0.73, 0.86) in the internal test set, 0.84 (95% CI: 0.77, 0.91) in the external cohort, and 0.78 (95% CI: 0.73, 0.83) in the treatment test cohort. Calibration curves and DCA confirmed the models' excellent discrimination, calibration, and potential clinical utility across internal and external datasets. The AUCs of both nuclear medicine physicians were lower than those of the best-performing deep learning model in external test set (AUC: 0.75 vs. 0.77 vs. 0.84).</p><p><strong>Conclusion: </strong>Deep learning models utilizing partial-body input from planar whole-body bone scintigraphy demonstrate high discriminatory power for diagnosing SBI in NPC patients, surpassing experienced nuclear medicine physicians.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390795","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":"Multiparametric MRI based deep learning model for prediction of early recurrence of hepatocellular carcinoma after SR following TACE.","authors":"Hongyu Wangi, Jinwei Li, Yushu Ouyang, He Ren, Chao An, Wendao Liu","doi":"10.1007/s00432-024-05941-w","DOIUrl":"10.1007/s00432-024-05941-w","url":null,"abstract":"<p><strong>Background: </strong>Surgical resection (SR) following transarterial chemoembolization (TACE) is a promising treatment for unresectable hepatocellular carcinoma (uHCC). However, biomarkers for the prediction of postoperative recurrence are needed.</p><p><strong>Purpose: </strong>To develop and validate a model combining deep learning (DL) and clinical data for early recurrence (ER) in uHCC patients after TACE.</p><p><strong>Methods: </strong>A total of 511 patients who received SR following TACE were assigned to derivation (n = 413) and validation (n = 98) cohorts. Deep learning features were taken from the largest tumor area in liver MRI. A nomogram using DL signatures and clinical data was made to forecast early recurrence risk in uHCC patients. Model performance was evaluated using area under the curve (AUC).</p><p><strong>Results: </strong>A total of 2278 subsequences and 31,346 slices multiparametric MRI including contrast-enhanced T1WI, T2WI and DWI were input in the DL model simultaneously. Multivariable analysis identified three independent predictors for the development of the nomogram: tumor number (hazard ratio [HR]:3.42, 95% confidence interval [CI]: 2.75-4.31, P = 0.003), microvascular invasion (HR: 9.21, 6.24-32.14; P < 0.001), and DL scores (HR: 17.46, 95% CI: 12.94-23.57, P < 0.001). The AUC of the nomogram was 0.872 and 0.862 in two cohorts, significantly outperforming single-subsequence-based DL mode and clinical model (all, P < 0.001). The nomogram provided two risk strata for cumulative overall survival in two cohorts, showing significant statistical results (P < 0.001).</p><p><strong>Conclusions: </strong>The DL-based nomogram is essential to identify patients with uHCC suitable for treatment with SR following TACE and may potentially benefit personalized decision-making.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390796","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}