Cancer Imaging最新文献

{"title":"Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point ¹⁸F-FDG PET/CT.","authors":"Yuhang Liu, Jian Wang, Bulin Du, Yaming Li, Xuena Li","doi":"10.1186/s40644-025-00834-8","DOIUrl":"10.1186/s40644-025-00834-8","url":null,"abstract":"<p><strong>Background: </strong>Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point ¹⁸F-FDG PET/CT to predict the malignant risk of GGOs.</p><p><strong>Methods: </strong>Retrospectively analyzing 311 patients with 397 GGOs, this study identified 118 low-risk GGOs and 279 high-risk GGOs through pathology and follow-up according to the new WHO classification. The dataset was randomly divided into a training set comprising 239 patients (318 lesions) and a testing set comprising 72 patients (79 lesions), we employed a self-configuring 3D nnU-net convolutional neural network with majority voting method to segment GGOs and predict malignant risk of GGOs. Three independent segmentation prediction models were developed based on thin-section lung CT, early-phase ¹⁸F-FDG PET/CT, and dual-time-point ¹⁸F-FDG PET/CT, respectively. Simultaneously, the results of the dual-time-point ¹⁸F-FDG PET/CT model on the testing set were compared with the diagnostic of nuclear medicine physicians.</p><p><strong>Results: </strong>The dual-time-point ¹⁸F-FDG PET/CT model achieving a Dice coefficient of 0.84 ± 0.02 for GGOs segmentation and demonstrating high accuracy (84.81%), specificity (84.62%), sensitivity (84.91%), and AUC (0.85) in predicting malignant risk. The accuracy of the thin-section CT model is 73.42%, and the accuracy of the early-phase ¹⁸F-FDG PET/CT model is 78.48%, both of which are lower than the accuracy of the dual-time-point ¹⁸F-FDG PET/CT model. The diagnostic accuracy for resident, junior and expert physicians were 67.09%, 74.68%, and 78.48%, respectively. The accuracy (84.81%) of the dual-time-point ¹⁸F-FDG PET/CT model was significantly higher than that of nuclear medicine physicians.</p><p><strong>Conclusions: </strong>Based on dual-time-point ¹⁸F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"17"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating biparametric MRI for diagnosing muscle-invasive bladder cancer with variant urothelial histology: a multicenter study.","authors":"Peikun Liu, Lingkai Cai, Hongliang Que, Meihua Jiang, Xuping Jiang, Bo Liang, Gongcheng Wang, Linjing Jiang, Xiao Yang, Qiang Lu","doi":"10.1186/s40644-025-00831-x","DOIUrl":"10.1186/s40644-025-00831-x","url":null,"abstract":"<p><strong>Background: </strong>Vesical Imaging-Reporting and Data System (VI-RADS) based on multiparametric MRI (mp-MRI) demonstrated excellent performance in diagnosing muscle-invasive bladder cancer (MIBC) in cases of pure urothelial carcinoma. However, the performance of VI-RADS based on mp-MRI and biparametric MRI (bp-MRI) in diagnosing urothelial carcinoma with variant histology (VUC) remains unknown.</p><p><strong>Purpose: </strong>To evaluate the applicability of VI-RADS using mp-MRI and bp-MRI in diagnosing MIBC in patients with VUC.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 86 patients with VUC from different medical centers. Each patient underwent mp-MRI, with images evaluated using VI-RADS scores. The acquired images were divided into two groups: the mp-MRI group and the bp-MRI group. The mp-MRI group was evaluated according to the VI-RADS protocol. For the bp-MRI group, two VI-RADS scoring criteria were established: bp-DWI, primarily driven by DWI, and bp-T2WI, primarily driven by T2WI. The bp-MRI group was evaluated based on these two criteria. Inter-reader agreement performance was evaluated using Kappa analysis. The evaluation methods were evaluated by receiver operating characteristic curve. Comparison of the area under the curve (AUC) was performed used DeLong's test. A p-value < 0.05 was considered significant.</p><p><strong>Results: </strong>Inter-reader agreement was high across all evaluation methods, with Kappa values exceeding 0.80. The AUCs for mp-MRI, bp-DWI, and bp-T2WI were 0.934, 0.885, and 0.932, respectively. The diagnostic performance of bp-T2WI was comparable with that of mp-MRI (p = 0.682) and significantly higher than bp-DWI (p = 0.007). Both mp-MRI and bp-T2WI demonstrated high sensitivity and specificity.</p><p><strong>Conclusion: </strong>VI-RADS based on mp-MRI demonstrates good diagnostic performance for MIBC in VUC patients. bp-T2WI may provide comparable diagnostic performance to mp-MRI.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"15"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11834218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating advanced renal cell carcinoma in the era of artificial intelligence.","authors":"Elie J Najem, Mohd Javed S Shaikh, Atul B Shinagare, Katherine M Krajewski","doi":"10.1186/s40644-025-00835-7","DOIUrl":"10.1186/s40644-025-00835-7","url":null,"abstract":"<p><strong>Background: </strong>Research has helped to better understand renal cell carcinoma and enhance management of patients with locally advanced and metastatic disease. More recently, artificial intelligence has emerged as a powerful tool in cancer research, particularly in oncologic imaging. BODY: Despite promising results of artificial intelligence in renal cell carcinoma research, most investigations have focused on localized disease, while relatively fewer studies have targeted advanced and metastatic disease. This paper summarizes major artificial intelligence advances focusing mostly on their potential clinical value from initial staging and identification of high-risk features to predicting response to treatment in advanced renal cell carcinoma, while addressing major limitations in the development of some models and highlighting new avenues for future research.</p><p><strong>Conclusion: </strong>Artificial intelligence-enabled models have a great potential in improving clinical practice in the diagnosis and management of advanced renal cell carcinoma, particularly when developed from both clinicopathologic and radiologic data.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.","authors":"Cong Ding, Yue Kang, Fan Bai, Genji Bai, Junfang Xian","doi":"10.1186/s40644-025-00837-5","DOIUrl":"10.1186/s40644-025-00837-5","url":null,"abstract":"<p><strong>Background: </strong>Immunotherapy has revolutionized the treatment landscape for head and neck squamous cell carcinoma (HNSCC) and PD-L1 combined positivity score (CPS) scoring is recommended as a biomarker for immunotherapy. Therefore, this study aimed to develop an MRI-based deep learning score (DLS) to non-invasively assess PD-L1 expression status in HNSCC patients and evaluate its potential effeciency in predicting prognostic stratification following treatment with immune checkpoint inhibitors (ICI).</p><p><strong>Methods: </strong>In this study, we collected data from four patient cohorts comprising a total of 610 HNSCC patients from two separate institutions. We developed deep learning models based on the ResNet-101 convolutional neural network to analyze three MRI sequences (T1WI, T2WI, and contrast-enhanced T1WI). Tumor regions were manually segmented, and features extracted from different MRI sequences were fused using a transformer-based model incorporating attention mechanisms. The model's performance in predicting PD-L1 expression was evaluated using the area under the curve (AUC), sensitivity, specificity, and calibration metrics. Survival analyses were conducted using Kaplan-Meier survival curves and log-rank tests to evaluate the prognostic significance of the DLS.</p><p><strong>Results: </strong>The DLS demonstrated high predictive accuracy for PD-L1 expression, achieving an AUC of 0.981, 0.860 and 0.803 in the training, internal and external validation cohort. Patients with higher DLS scores demonstrated significantly improved progression-free survival (PFS) in both the internal validation cohort (hazard ratio: 0.491; 95% CI, 0.270-0.892; P = 0.005) and the external validation cohort (hazard ratio: 0.617; 95% CI, 0.391-0.973; P = 0.040). In the ICI-treated cohort, the DLS achieved an AUC of 0.739 for predicting durable clinical benefit (DCB).</p><p><strong>Conclusions: </strong>The proposed DLS offered a non-invasive and accurate approach for assessing PD-L1 expression in patients with HNSCC and effectively stratified HNSCC patients to benefit from immunotherapy based on PFS.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"14"},"PeriodicalIF":3.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143432431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography.","authors":"Xian-Ya Zhang, Di Zhang, Wang Zhou, Zhi-Yuan Wang, Chao-Xue Zhang, Jin Li, Liang Wang, Xin-Wu Cui","doi":"10.1186/s40644-025-00832-w","DOIUrl":"10.1186/s40644-025-00832-w","url":null,"abstract":"<p><strong>Background: </strong>To develop a model based on intra- and peritumoral radiomics features derived from B-mode ultrasound (BMUS), strain elastography (SE), and shear wave elastography (SWE) for cervical lymph node metastasis (LNM) prediction in papillary thyroid cancer (PTC) and to determine the optimal peritumoral size.</p><p><strong>Methods: </strong>PTC Patients were enrolled from two medical centers. Radiomics features were extracted from intratumoral and four peritumoral regions with widths of 0.5-2.0 mm on tri-modality ultrasound (US) images. Boruta algorithm and XGBoost classifier were used for features selection and radiomics signature (RS) construction, respectively. A hybrid model combining the optimal RS with the highest AUC and clinical characteristics as well as a clinical model were built via multivariate logistic regression analysis. The performance of the established models was evaluated by discrimination, calibration, and clinical utility. DeLong's test was used for performance comparison. The diagnostic augmentation of two radiologists with hybrid model's assistance was also evaluated.</p><p><strong>Results: </strong>A total of 660 patients (mean age, 41 years ± 12 [SD]; 506 women) were divided into training, internal test and external test cohorts. The multi-modality RS_1.0 mm yielded the optimal AUCs of 0.862, 0.798 and 0.789 across the three cohorts, outperforming other single-modality RSs and intratumoral RS. The AUCs of the hybrid model integrating multi-modality RS_1.0 mm, age, gender, tumor size and microcalcification were 0.883, 0.873 and 0.841, respectively, which were significantly superior to other RSs and clinical model (all p < 0.05). The hybrid model assisted to significantly improve the sensitivities of junior and senior radiologists by 19.7% and 18.3%, respectively (all p < 0.05).</p><p><strong>Conclusions: </strong>The intra-peritumoral radiomics model based on tri-modality US imaging holds promise for improving risk stratification and guiding treatment strategies in PTC.</p><p><strong>Trial registration: </strong>Retrospectively registered.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"13"},"PeriodicalIF":3.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"¹⁸F-FDG PET/CT metabolic parameters are correlated with clinical features and valuable in clinical stratification management in patients of castleman disease.","authors":"Guolin Wang, Qianhe Xu, Yinuo Liu, Huatao Wang, Fei Yang, Zhenfeng Liu, Xinhui Su","doi":"10.1186/s40644-025-00833-9","DOIUrl":"10.1186/s40644-025-00833-9","url":null,"abstract":"<p><strong>Background: </strong>Castleman disease (CD) is a rare lymphoproliferative disorder. This study is to evaluate the correlation between ¹⁸F-flurodeoxyglucose (¹⁸F-FDG) positron emission tomography-computed tomography (PET/CT) and clinical features in CD patients, and exploring its value in distinguishing disease severity and assisting in risk stratification.</p><p><strong>Methods: </strong>We retrospectively enrolled 93 patients with newly diagnosed CD. Traditional semi-quantitative ¹⁸F-FDG PET/CT parameters including the maximum standardized uptake value (SUV_max), total metabolic lesion volume (MLV), total lesion glycolysis (TLG) were measured, and the lymph node to liver ratio of SUV_max (LLR), lymph node to mediastinal blood pool of SUV_max (LMR), spleen to liver ratio of SUV_max (SLR) and No. of involved lymph node stations (LNS) were calculated. The correlation between these metabolic parameters and clinical features were studied using a univariate analysis. The influencing factors of CD severity were determined by univariate and multivariate analysis. The optimal cut-off values for metabolic parameters were obtained by receiver operating characteristic (ROC) curve.</p><p><strong>Results: </strong>A total of 20 unicentric CD (UCD) and 73 multicentric CD (MCD) cases were included, with the highest SUV_max of Lymph nodes ranged 1.40 ~ 28.18 (median, 4.86). The metabolic parameters (SUV_max, MLV, TLG, LLR, LMR, SLR) in MCD were significantly higher than those in UCD (p < 0.05). There were significant differences in MLV, TLG, LLR and SLR among different histological subtypes (p < 0.05). The No. of involved lymph node stations (LNS) and spleen-to-liver ratio (SLR) were significantly correlated with laboratory findings. In univariate and multivariate analyses, SLR (p = 0.011; OR value = 14.806) and HGB (p = 0.004; OR value = 0.044) exhibited an independent correlation with disease severity. The ROC curve revealed that SLR had a sensitivity of 77.4%, specificity of 69.4% and AUC of 0.761 (cut-off value = 1.04; p < 0.001) in discriminating severity of CD. SLR also showed significant statistical differences between severe and non-severe idiopathic MCD (iMCD) (p = 0.016).</p><p><strong>Conclusions: </strong>SLR is closely related to clinical features of CD, and can relatively effectively differentiate the severity of CD and assist in the clinical risk stratification of iMCD.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"12"},"PeriodicalIF":3.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143405763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative prediction of IDH genotypes and prognosis in adult-type diffuse gliomas: intratumor heterogeneity habitat analysis using dynamic contrast-enhanced MRI and diffusion-weighted imaging.","authors":"Xingrui Wang, Zhenhui Xie, Xiaoqing Wang, Yang Song, Shiteng Suo, Yan Ren, Wentao Hu, Yi Zhu, Mengqiu Cao, Yan Zhou","doi":"10.1186/s40644-025-00829-5","DOIUrl":"10.1186/s40644-025-00829-5","url":null,"abstract":"<p><strong>Background: </strong>Intratumor heterogeneity (ITH) is a key biological characteristic of gliomas. This study aimed to characterize ITH in adult-type diffuse gliomas and assess the feasibility of using habitat imaging based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) to preoperatively predict isocitrate dehydrogenase (IDH) genotypes and prognosis.</p><p><strong>Methods: </strong>Sixty-three adult-type diffuse gliomas with known IDH genotypes were enrolled. Volume transfer constant (K^trans) and apparent diffusion coefficient (ADC) maps were acquired from DCE-MRI and DWI, respectively. After tumor segmentation, the k-means algorithm clustered K^trans and ADC image voxels to generate spatial habitats and extract quantitative image features. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to evaluate IDH predictive performance. Multivariable logistic regression models were constructed and validated using leave-one-out cross-validation, and the contrast-enhanced subgroup was analyzed independently. Kaplan-Meier and Cox proportional hazards regression analyses were used to investigate the relationship between tumor habitats and progression-free survival (PFS) in the two IDH groups.</p><p><strong>Results: </strong>Three habitats were identified: Habitat 1 (hypo-vasopermeability and hyper-cellularity), Habitat 2 (hypo-vasopermeability and hypo-cellularity), and Habitat 3 (hyper-vasopermeability). Compared to the IDH wild-type group, the IDH mutant group exhibited lower mean K^trans values in Habitats 1 and 2 (both P < 0.001), higher volume (P < 0.05) and volume percentage (pVol, P < 0.01) of Habitat 2, and lower volume and pVol of Habitat 3 (both P < 0.001). The optimal logistic regression model for IDH prediction yielded an AUC of 0.940 (95% confidence interval [CI]: 0.880-1.000), which improved to 0.948 (95% CI: 0.890-1.000) after cross-validation. Habitat 2 contributed the most to the model, consistent with the findings in the contrast-enhanced subgroup. In IDH wild-type group, pVol of Habitat 2 was identified as a significant risk factor for PFS (high- vs. low-pVol subgroup, hazard ratio = 2.204, 95% CI: 1.061-4.580, P = 0.034), with a value below 0.26 indicating a 5-month median survival benefit.</p><p><strong>Conclusions: </strong>Habitat imaging employing DCE-MRI and DWI may facilitate the characterization of ITH in adult-type diffuse gliomas and serve as a valuable adjunct in the preoperative prediction of IDH genotypes and prognosis.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"11"},"PeriodicalIF":3.5,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11807326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery.","authors":"Jin-Can Huang, Shao-Cheng Lyu, Bing Pan, Han-Xuan Wang, You-Wei Ma, Tao Jiang, Qiang He, Ren Lang","doi":"10.1186/s40644-025-00830-y","DOIUrl":"10.1186/s40644-025-00830-y","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pancreatic cancer (BRPC). In the present study, we establish a logistic regression model, aiming to predict long-term survival and identify related prognostic factors in patients with BRPC who underwent upfront surgery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Medical records of patients with BRPC who underwent upfront surgery with portal vein resection and reconstruction from Jan. 2011 to Dec. 2020 were reviewed. Based on postoperative overall survival (OS), patients were divided into the short-term group (≤ 2 years) and the long-term group (&gt; 2 years). Univariate and multivariate analyses were performed to compare perioperative variables and long-term prognoses between groups to identify related independent prognostic factors. All patients are randomly divided into the training set and the validation set at a 7:3 ratio. The logistic regression model was established and evaluated for accuracy through the above variables in the training set and the validation set, respectively, and was visualized by Nomograms. Meanwhile, the model was further verified and compared for accuracy, the area under the curve (AUC) of the receiver operating characteristic curves (ROC), and calibration analysis. Then, we plotted and sorted perioperative variables by SHAP value to identify the most important variables. The first 4 most important variables were compared with the above independent prognostic factors. Finally, other models including support vector machines (SVM), random forest, decision tree, and XGBoost were also constructed using the above 4 variables. 10-fold stratified cross-validation and the AUC of ROC were performed to compare accuracy between models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;104 patients were enrolled in the study, and the median OS was 15.5 months, the 0.5-, 1-, and 2- years OS were 81.7%, 57.7%, and 30.8%, respectively. In the long-term group (n = 32) and short-term group (n = 72), the overall median survival time and the 1-, 2-, 3- years overall survival were 38 months, 100%, 100%, 61.3% and 10 months, 38.9%, 0%, 0%, respectively. 4 variables, including age, vascular invasion length, vascular morphological malformation, and local lymphadenopathy were confirmed as independent risk factors between the two groups following univariate and multivariate analysis. The AUC between the training set (n = 72) and the validation set (n = 32) were 0.881 and 0.875. SHAP value showed that the above variables were the first 4 most important. The AUC following 10-fold stratified cross-validation in the logistic regression (0.864) is better than SVM (0.693), random forest (0.789), decision tree (0.790), and XGBoost (0.726).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Age, ","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"10"},"PeriodicalIF":3.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature.","authors":"Ting Yan, Zhenpeng Yan, Guohui Chen, Songrui Xu, Chenxuan Wu, Qichao Zhou, Guolan Wang, Ying Li, Mengjiu Jia, Xiaofei Zhuang, Jie Yang, Lili Liu, Lu Wang, Qinglu Wu, Bin Wang, Tianyi Yan","doi":"10.1186/s40644-024-00821-5","DOIUrl":"10.1186/s40644-024-00821-5","url":null,"abstract":"<p><strong>Background: </strong>The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.</p><p><strong>Methods: </strong>A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.</p><p><strong>Results: </strong>A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.</p><p><strong>Conclusion: </strong>An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"9"},"PeriodicalIF":3.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nomogram based on dual-energy computed tomography to predict the response to induction chemotherapy in patients with nasopharyngeal carcinoma: a two-center study.","authors":"Huanhuan Ren, Junhao Huang, Yao Huang, Bangyuan Long, Mei Zhang, Jing Zhang, Huarong Li, Tingting Huang, Daihong Liu, Ying Wang, Jiuquan Zhang","doi":"10.1186/s40644-025-00827-7","DOIUrl":"10.1186/s40644-025-00827-7","url":null,"abstract":"<p><strong>Background: </strong>Previous studies utilizing dual-energy CT (DECT) for evaluating treatment efficacy in nasopharyngeal cancinoma (NPC) are limited. This study aimed to investigate whether the parameters from DECT can predict the response to induction chemotherapy in NPC patients in two centers.</p><p><strong>Methods: </strong>This two-center retrospective study included patients diagnosed with NPC who underwent contrast-enhanced DECT between March 2019 and November 2023. The clinical and DECT-derived parameters of tumor lesions were calculated to predict the response. We employed univariate and multivariate analysis to identify significant factors. Subsequently, the clinical, DECT, and clinical-DECT nomogram models were developed using independent predictors in the training cohort and validated in the test cohort. Receiver operating characteristic analysis was performed to evaluate the models' performance.</p><p><strong>Results: </strong>A total of 321 patients were included in the study, predominantly male [247 (76.9%)] with an average age of 52.04 ± 10.87 years. The training cohort (Center 1) comprised 252 patients, while the test cohort (Center 2) comprised 69 patients. Of these, 233 out of 321 patients (72.6%) were responders to induction chemotherapy. The clinical-DECT nomogram showed an AUC of 0.805 (95% CI, 0.688-0.906), outperforming both the DECT model (Extracellular volume fraction [ECVf]) (AUC, 0.706 [95% CI, 0.571-0.825]) and the clinical model (Ki67) (AUC, 0.693 [95% CI, 0.580-0.806]) in the test cohort.</p><p><strong>Conclusions: </strong>Ki67 and ECVf emerged as independent predictive factors for response to induction chemotherapy in NPC patients. The proposed nomogram, incorporating ECVf, demonstrated accurate prediction of treatment response.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"8"},"PeriodicalIF":3.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}