Cancer Imaging最新文献

筛选
英文 中文
MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy. 核磁共振成像放射组学和营养-炎症生物标志物:预测同时接受放化疗的宫颈癌患者无进展生存期的强大组合。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-24 DOI: 10.1186/s40644-024-00789-2
Qi Yan, Menghan- Wu, Jing Zhang, Jiayang- Yang, Guannan- Lv, Baojun- Qu, Yanping- Zhang, Xia Yan, Jianbo- Song
{"title":"MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy.","authors":"Qi Yan, Menghan- Wu, Jing Zhang, Jiayang- Yang, Guannan- Lv, Baojun- Qu, Yanping- Zhang, Xia Yan, Jianbo- Song","doi":"10.1186/s40644-024-00789-2","DOIUrl":"10.1186/s40644-024-00789-2","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.</p><p><strong>Methods: </strong>We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.</p><p><strong>Conclusions: </strong>The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"144"},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495704","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}
引用次数: 0
Current trends in the characterization and monitoring of vascular response to cancer therapy. 表征和监测血管对癌症治疗反应的当前趋势。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-23 DOI: 10.1186/s40644-024-00767-8
Binita Shrestha, Noah B Stern, Annie Zhou, Andrew Dunn, Tyrone Porter
{"title":"Current trends in the characterization and monitoring of vascular response to cancer therapy.","authors":"Binita Shrestha, Noah B Stern, Annie Zhou, Andrew Dunn, Tyrone Porter","doi":"10.1186/s40644-024-00767-8","DOIUrl":"10.1186/s40644-024-00767-8","url":null,"abstract":"<p><p>Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"143"},"PeriodicalIF":3.5,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495702","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}
引用次数: 0
Cross-institutional evaluation of deep learning and radiomics models in predicting microvascular invasion in hepatocellular carcinoma: validity, robustness, and ultrasound modality efficacy comparison. 预测肝细胞癌微血管侵犯的深度学习和放射组学模型的跨机构评估:有效性、稳健性和超声模式疗效比较。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-22 DOI: 10.1186/s40644-024-00790-9
Weibin Zhang, Qihui Guo, Yuli Zhu, Meng Wang, Tong Zhang, Guangwen Cheng, Qi Zhang, Hong Ding
{"title":"Cross-institutional evaluation of deep learning and radiomics models in predicting microvascular invasion in hepatocellular carcinoma: validity, robustness, and ultrasound modality efficacy comparison.","authors":"Weibin Zhang, Qihui Guo, Yuli Zhu, Meng Wang, Tong Zhang, Guangwen Cheng, Qi Zhang, Hong Ding","doi":"10.1186/s40644-024-00790-9","DOIUrl":"10.1186/s40644-024-00790-9","url":null,"abstract":"<p><strong>Purpose: </strong>To conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation.</p><p><strong>Methods: </strong>This retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS). For radiomics, we constructed models with enlarged vs. original regions of interest (ROIs). A cross-validation approach was performed by training models on one center's dataset and validating the other, and vice versa. This allowed assessment of the validity of different ultrasound modalities and the cross-center robustness of the models. The optimal model combined with alpha-fetoprotein (AFP) was also validated. The head-to-head comparison was based on the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Thirteen DL models and 25 radiomics models using different ultrasound modalities were constructed and compared. B + CEUS was the optimal modality for both DL and radiomics models. The DL model achieved AUCs of 0.802-0.818 internally and 0.667-0.688 externally across the two centers, whereas radiomics achieved AUCs of 0.749-0.869 internally and 0.646-0.697 externally. The radiomics models showed overall improvement with enlarged ROIs (P < 0.05 for both CEUS and B + CEUS modalities). The DL models showed good cross-institutional robustness (P > 0.05 for all modalities, 1.6-2.1% differences in AUC for the optimal modality), whereas the radiomics models had relatively limited robustness across the two centers (12% drop-off in AUC for the optimal modality). Adding AFP improved the DL models (P < 0.05 externally) and well maintained the robustness, but did not benefit the radiomics model (P > 0.05).</p><p><strong>Conclusion: </strong>Cross-institutional validation indicated that DL demonstrated better robustness than radiomics for preoperative MVI prediction in patients with HCC, representing a promising solution to non-standardized ultrasound examination procedures.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"142"},"PeriodicalIF":3.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495701","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}
引用次数: 0
Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer. 用于预测实性 I 期非小细胞肺癌术后进展的多模态深度学习放射组学模型。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-17 DOI: 10.1186/s40644-024-00783-8
Qionglian Kuang, Bao Feng, Kuncai Xu, Yehang Chen, Xiaojuan Chen, Xiaobei Duan, Xiaoyan Lei, Xiangmeng Chen, Kunwei Li, Wansheng Long
{"title":"Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.","authors":"Qionglian Kuang, Bao Feng, Kuncai Xu, Yehang Chen, Xiaojuan Chen, Xiaobei Duan, Xiaoyan Lei, Xiangmeng Chen, Kunwei Li, Wansheng Long","doi":"10.1186/s40644-024-00783-8","DOIUrl":"https://doi.org/10.1186/s40644-024-00783-8","url":null,"abstract":"<p><strong>Purpose: </strong>To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).</p><p><strong>Materials and methods: </strong>A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significant differences were used as input variables for the extreme learning machine (ELM) classifier to construct the clinical model. We used the transfer learning strategy to train the ResNet18 model, used the model to extract deep learning features from all CT images, and then used the ELM classifier to classify the deep learning features to obtain the deep learning signature (DLS). A MDLR model incorporating clinicopathological characteristics, subjective CT findings and DLS was constructed. The diagnostic efficiencies of the clinical model, DLS model and MDLR model were evaluated by the area under the curve (AUC).</p><p><strong>Results: </strong>Univariate analysis indicated that size (p = 0.004), neuron-specific enolase (NSE) (p = 0.03), carbohydrate antigen 19 - 9 (CA199) (p = 0.003), and pathological stage (p = 0.027) were significantly associated with the progression of solid stage I NSCLC after surgery. Therefore, these clinical characteristics were incorporated into the clinical model to predict the risk of progression in postoperative solid-stage NSCLC patients. A total of 294 deep learning features with nonzero coefficients were selected. The DLS in the progressive group was (0.721 ± 0.371), which was higher than that in the nonprogressive group (0.113 ± 0.350) (p < 0.001). The combination of size、NSE、CA199、pathological stage and DLS demonstrated the superior performance in differentiating postoperative progression status. The AUC of the MDLR model was 0.885 (95% confidence interval [CI]: 0.842-0.927), higher than that of the clinical model (0.675 (95% CI: 0.599-0.752)) and DLS model (0.882 (95% CI: 0.835-0.929)). The DeLong test and decision in curve analysis revealed that the MDLR model was the most predictive and clinically useful model.</p><p><strong>Conclusion: </strong>MDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"140"},"PeriodicalIF":3.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142485733","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}
引用次数: 0
Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ? 通过基于计算机断层扫描的放射组学分析预测食管鳞状细胞癌的淋巴管侵犯:二维还是三维?
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-17 DOI: 10.1186/s40644-024-00786-5
Yang Li, Xiaolong Gu, Li Yang, Xiangming Wang, Qi Wang, Xiaosheng Xu, Andu Zhang, Meng Yue, Mingbo Wang, Mengdi Cong, Jialiang Ren, Wei Ren, Gaofeng Shi
{"title":"Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?","authors":"Yang Li, Xiaolong Gu, Li Yang, Xiangming Wang, Qi Wang, Xiaosheng Xu, Andu Zhang, Meng Yue, Mingbo Wang, Mengdi Cong, Jialiang Ren, Wei Ren, Gaofeng Shi","doi":"10.1186/s40644-024-00786-5","DOIUrl":"https://doi.org/10.1186/s40644-024-00786-5","url":null,"abstract":"<p><strong>Background: </strong>To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).</p><p><strong>Methods: </strong>Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors' 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).</p><p><strong>Results: </strong>There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.</p><p><strong>Conclusions: </strong>Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"141"},"PeriodicalIF":3.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458638","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}
引用次数: 0
Qualitative and quantitative analysis of solid renal tumors by high-frame-rate contrast-enhanced ultrasound. 通过高帧率对比增强超声对实体肾肿瘤进行定性和定量分析。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-15 DOI: 10.1186/s40644-024-00788-3
Hailan Wu, Jiayu Shi, Long Gao, Jingling Wang, WenXin Yuan, WeiPing Zhang, Zhixing Liu, Yi Mao
{"title":"Qualitative and quantitative analysis of solid renal tumors by high-frame-rate contrast-enhanced ultrasound.","authors":"Hailan Wu, Jiayu Shi, Long Gao, Jingling Wang, WenXin Yuan, WeiPing Zhang, Zhixing Liu, Yi Mao","doi":"10.1186/s40644-024-00788-3","DOIUrl":"https://doi.org/10.1186/s40644-024-00788-3","url":null,"abstract":"<p><strong>Objective: </strong>To analyze the characteristics of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in solid renal tumors using qualitative and quantitative methods.</p><p><strong>Methods: </strong>Seventy-five patients who underwent preoperative conventional ultrasound (US), conventional contrast-enhanced ultrasound (C-CEUS), and H-CEUS examination of renal tumors were retrospectively analyzed, with a total of 89 renal masses. The masses were divided into the benign (30 masses) and malignant groups (59 masses) based on the results of enhanced computer tomography and pathology. The location, diameter, shape, border, calcification, and color doppler blood flow imaging (CDFI) of the lesions were observed by US, and the characteristics of the C-CEUS and H-CEUS images were qualitatively and quantitatively analyzed. The χ² test or Fisher's exact probability method was used to compare the US image characteristics between the benign and malignant groups, and the image characteristics of C-CEUS and H-CEUS between the benign and malignant groups. Moreover, the nonparametric Mann-Whitney test was used to compare the differences in C-CEUS and H-CEUS time-intensity curve (TIC) parameters.</p><p><strong>Results: </strong>Significant differences in gender, surgical approach, echogenicity, and CDFI were observed between the malignant and benign groups (p = 0.003, < 0.001, < 0.001, = 0003). Qualitative analysis also revealed significant differences in the mode of wash-out and fill-in direction between C-CEUS and H-CEUS in the malignant group (p = 0.041, 0.002). In addition, the homogeneity of enhancement showed significant differences between the two contrast models in the benign group (p = 0.009). Quantitative analysis indicated that the TIC parameters peak intensity (PI), deceleration time (DT) /2, area under the curve (AUC), and mean transition time (MTT) were significantly lower in the H-CEUS model compared to the C-CEUS model in both the benign and malignant groups. (all p < 0.001). In contrast, ascending slope of rise curve (AS) was significantly higher in the H-CEUS model compared to the C-CEUS model in the malignant group (p = 0.048).</p><p><strong>Conclusions: </strong>In renal tumors, H-CEUS shows clearer internal enhancement of the mass and the changes in the wash-out period. The quantitative TIC parameters PI, DT/2, AUC, and MTT were lower in H-CEUS compared to C-CEUS. Both the quantitative and qualitative analyses indicated that H-CEUS better displays the characteristics of solid renal masses compared with C-CEUS.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"139"},"PeriodicalIF":3.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11481758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458639","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}
引用次数: 0
Correction: Contrast enhanced ultrasound of liver lesions in patients treated for childhood malignancies. 更正:儿童恶性肿瘤患者肝脏病变的对比增强超声检查。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-14 DOI: 10.1186/s40644-024-00785-6
Ayatullah G Mostafa, Zachary Abramson, Mina Ghbrial, Som Biswas, Sherwin Chan, Himani Darji, Jessica Gartrell, Seth E Karol, Yimei Li, Daniel A Mulrooney, Tushar Patni, Tarek M Zaghloul, M Beth McCarville
{"title":"Correction: Contrast enhanced ultrasound of liver lesions in patients treated for childhood malignancies.","authors":"Ayatullah G Mostafa, Zachary Abramson, Mina Ghbrial, Som Biswas, Sherwin Chan, Himani Darji, Jessica Gartrell, Seth E Karol, Yimei Li, Daniel A Mulrooney, Tushar Patni, Tarek M Zaghloul, M Beth McCarville","doi":"10.1186/s40644-024-00785-6","DOIUrl":"10.1186/s40644-024-00785-6","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"138"},"PeriodicalIF":3.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458637","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}
引用次数: 0
Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer. FDG PET/CT 上 SUVpeak 与肿瘤中心点距离对预测乳腺癌患者新辅助化疗反应的临床价值。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-11 DOI: 10.1186/s40644-024-00787-4
Sun-Pyo Hong, Sang Mi Lee, Ik Dong Yoo, Jong Eun Lee, Sun Wook Han, Sung Yong Kim, Jeong Won Lee
{"title":"Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer.","authors":"Sun-Pyo Hong, Sang Mi Lee, Ik Dong Yoo, Jong Eun Lee, Sun Wook Han, Sung Yong Kim, Jeong Won Lee","doi":"10.1186/s40644-024-00787-4","DOIUrl":"10.1186/s40644-024-00787-4","url":null,"abstract":"<p><strong>Background: </strong>Since it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients.</p><p><strong>Methods: </strong>This study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters.</p><p><strong>Results: </strong>Of 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS.</p><p><strong>Conclusion: </strong>NHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"136"},"PeriodicalIF":3.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406159","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}
引用次数: 0
Multimodal apparent diffusion MRI model in noninvasive evaluation of breast cancer and Ki-67 expression. 无创评估乳腺癌和 Ki-67 表达的多模态表观弥散 MRI 模型
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-11 DOI: 10.1186/s40644-024-00780-x
Huan Chang, Jinming Chen, Dawei Wang, Hongxia Li, Lei Ming, Yuting Li, Dan Yu, Yu Xin Yang, Peng Kong, Wenjing Jia, Qingqing Yan, Xinhui Liu, Qingshi Zeng
{"title":"Multimodal apparent diffusion MRI model in noninvasive evaluation of breast cancer and Ki-67 expression.","authors":"Huan Chang, Jinming Chen, Dawei Wang, Hongxia Li, Lei Ming, Yuting Li, Dan Yu, Yu Xin Yang, Peng Kong, Wenjing Jia, Qingqing Yan, Xinhui Liu, Qingshi Zeng","doi":"10.1186/s40644-024-00780-x","DOIUrl":"10.1186/s40644-024-00780-x","url":null,"abstract":"<p><strong>Background: </strong>To assess the capability of multimodal apparent diffusion (MAD) weighted magnetic resonance imaging (MRI) to distinguish between malignant and benign breast lesions, and to predict Ki-67 expression level in breast cancer.</p><p><strong>Methods: </strong>This retrospective study was conducted with 93 patients who had postoperative pathology-confirmed breast cancer or benign breast lesions. MAD images were acquired using a 3.0 T MRI scanner with 16 b values. The MAD parameters, as flow (f<sub>F</sub>, D<sub>F</sub>), unimpeded (fluid) (f<sub>UI</sub>), hindered (f<sub>H</sub>, D<sub>H</sub>, and α<sub>H</sub>), and restricted (f<sub>R</sub>, D<sub>R</sub>), were calculated. The differences of the parameters were compared by Mann-Whitney U test between the benign/malignant lesions and high/low Ki-67 expression level. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>The f<sub>R</sub> in the malignant lesions was significantly higher than in the benign lesions (P = 0.001), whereas the f<sub>UI</sub> and D<sub>H</sub> were found to be significantly lower (P = 0.007 and P < 0.001, respectively). Compared with individual parameter in differentiating malignant from benign breast lesions, the combination parameters of MAD (f<sub>R</sub>, D<sub>H</sub>, and f<sub>UI</sub>) provided the highest AUC (0.851). Of the 73 malignant lesions, 42 (57.5%) were assessed as Ki-67 low expression and 31 (42.5%) were Ki-67 high expression. The Ki-67 high status showed lower D<sub>H</sub>, higher D<sub>F</sub> and higher α<sub>H</sub> (P < 0.05). The combination parameters of D<sub>H</sub>, D<sub>F</sub>, and α<sub>H</sub> provided the highest AUC (0.691) for evaluating Ki-67 expression level.</p><p><strong>Conclusions: </strong>MAD weighted MRI is a useful method for the breast lesions diagnostics and the preoperative prediction of Ki-67 expression level.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"137"},"PeriodicalIF":3.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406160","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}
引用次数: 0
A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI. 基于 X 光、CT 和 MRI 的不完整多模态图像的深度学习模型,用于增强原发性骨肿瘤的分类。
IF 3.5 2区 医学
Cancer Imaging Pub Date : 2024-10-10 DOI: 10.1186/s40644-024-00784-7
Liwen Song, Chuanpu Li, Lilian Tan, Menghong Wang, Xiaqing Chen, Qiang Ye, Shisi Li, Rui Zhang, Qinghai Zeng, Zhuoyao Xie, Wei Yang, Yinghua Zhao
{"title":"A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI.","authors":"Liwen Song, Chuanpu Li, Lilian Tan, Menghong Wang, Xiaqing Chen, Qiang Ye, Shisi Li, Rui Zhang, Qinghai Zeng, Zhuoyao Xie, Wei Yang, Yinghua Zhao","doi":"10.1186/s40644-024-00784-7","DOIUrl":"10.1186/s40644-024-00784-7","url":null,"abstract":"<p><strong>Background: </strong>Accurately classifying primary bone tumors is crucial for guiding therapeutic decisions. The National Comprehensive Cancer Network guidelines recommend multimodal images to provide different perspectives for the comprehensive evaluation of primary bone tumors. However, in clinical practice, most patients' medical multimodal images are often incomplete. This study aimed to build a deep learning model using patients' incomplete multimodal images from X-ray, CT, and MRI alongside clinical characteristics to classify primary bone tumors as benign, intermediate, or malignant.</p><p><strong>Methods: </strong>In this retrospective study, a total of 1305 patients with histopathologically confirmed primary bone tumors (internal dataset, n = 1043; external dataset, n = 262) were included from two centers between January 2010 and December 2022. We proposed a Primary Bone Tumor Classification Transformer Network (PBTC-TransNet) fusion model to classify primary bone tumors. Areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the model's classification performance.</p><p><strong>Results: </strong>The PBTC-TransNet fusion model achieved satisfactory micro-average AUCs of 0.847 (95% CI: 0.832, 0.862) and 0.782 (95% CI: 0.749, 0.817) on the internal and external test sets. For the classification of benign, intermediate, and malignant primary bone tumors, the model respectively achieved AUCs of 0.827/0.727, 0.740/0.662, and 0.815/0.745 on the internal/external test sets. Furthermore, across all patient subgroups stratified by the distribution of imaging modalities, the PBTC-TransNet fusion model gained micro-average AUCs ranging from 0.700 to 0.909 and 0.640 to 0.847 on the internal and external test sets, respectively. The model showed the highest micro-average AUC of 0.909, accuracy of 84.3%, micro-average sensitivity of 84.3%, and micro-average specificity of 92.1% in those with only X-rays on the internal test set. On the external test set, the PBTC-TransNet fusion model gained the highest micro-average AUC of 0.847 for patients with X-ray + CT.</p><p><strong>Conclusions: </strong>We successfully developed and externally validated the transformer-based PBTC-Transnet fusion model for the effective classification of primary bone tumors. This model, rooted in incomplete multimodal images and clinical characteristics, effectively mirrors real-life clinical scenarios, thus enhancing its strong clinical practicability.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"135"},"PeriodicalIF":3.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399497","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}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信