Cancer Imaging最新文献

{"title":"A call for objectivity: Radiologists' proposed wishlist for response evaluation in solid tumors (RECIST 1.1).","authors":"Kathleen Ruchalski, Jordan M Anaokar, Matthias R Benz, Rohit Dewan, Michael L Douek, Jonathan G Goldin","doi":"10.1186/s40644-024-00802-8","DOIUrl":"10.1186/s40644-024-00802-8","url":null,"abstract":"<p><p>The Response Evaluation in Solid Tumors (RECIST) 1.1 provides key guidance for performing imaging response assessment and defines image-based outcome metrics in oncology clinical trials, including progression free survival. In this framework, tumors identified on imaging are designated as either target lesions, non-target disease or new lesions and a structured categorical response is assigned at each imaging time point. While RECIST provides definitions for these categories, it specifically and objectively defines only the target disease. Predefined thresholds of size change provide unbiased metrics for determining objective response and disease progression of the target lesions. However, worsening of non-target disease or emergence of new lesions is given the same importance in determining disease progression despite these being qualitatively assessed and less rigorously defined. The subjective assessment of non-target and new disease contributes to reader variability, which can impact the quality of image interpretation and even the determination of progression free survival. The RECIST Working Group has made significant efforts in developing RECIST 1.1 beyond its initial publication, particularly in its application to targeted agents and immunotherapy. A review of the literature highlights that the Working Group has occasionally employed or adopted objective measures for assessing non-target and new lesions in their evaluation of RECIST-based outcome measures. Perhaps a prospective evaluation of these more objective definitions for non-target and new lesions within the framework of RECIST 1.1 might improve reader interpretation. Ideally, these changes could also better align with clinically meaningful outcome measures of patient survival or quality of life.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"154"},"PeriodicalIF":3.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615196","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 ¹⁸F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.","authors":"Ping Yuan, Zhen-Hao Huang, Yun-Hai Yang, Fei-Chao Bao, Ke Sun, Fang-Fang Chao, Ting-Ting Liu, Jing-Jing Zhang, Jin-Ming Xu, Xiang-Nan Li, Feng Li, Tao Ma, Hao Li, Zi-Hao Li, Shan-Feng Zhang, Jian Hu, Yu Qi","doi":"10.1186/s40644-024-00799-0","DOIUrl":"10.1186/s40644-024-00799-0","url":null,"abstract":"<p><strong>Background: </strong>To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC).</p><p><strong>Methods: </strong>The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature.</p><p><strong>Results: </strong>The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively.</p><p><strong>Conclusions: </strong>Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"153"},"PeriodicalIF":3.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11556142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615193","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 first-line VEGFR-TKI resistance and survival in metastatic clear cell renal cell carcinoma using a clinical-radiomic nomogram.","authors":"Yichen Wang, Xinxin Zhang, Sicong Wang, Hongzhe Shi, Xinming Zhao, Yan Chen","doi":"10.1186/s40644-024-00792-7","DOIUrl":"10.1186/s40644-024-00792-7","url":null,"abstract":"<p><strong>Background: </strong>This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients.</p><p><strong>Materials and methods: </strong>In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test.</p><p><strong>Results: </strong>The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p < 0.001) and OS (HR 0.38 (0.20, 0.71), p = 0.002), in training set. PFS in low-risk group were significantly longer than high-risk group in training (p < 0.001) and test (p < 0.001) set, respectively. OS in low-risk group were significantly longer than high-risk group in training (p = 0.003) and test (p = 0.009) set, respectively.</p><p><strong>Conclusion: </strong>A nomogram combining baseline radiomic signature and clinical factors helped detecting first-line VEGFR-TKI early resistance and predicting short and long-term prognosis in mccRCC patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"151"},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615202","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":"Assessing the intracranial metabolic score as a novel prognostic tool in primary CNS lymphoma with end of induction-chemotherapy ¹⁸F-FDG PET/CT and PET/MR.","authors":"Yiwen Mo, Yongjiang Li, Yuqian Huang, Mingshi Chen, Chao Zhou, Xinling Li, Yuan Wei, Ruping Li, Wei Fan, Xu Zhang","doi":"10.1186/s40644-024-00798-1","DOIUrl":"10.1186/s40644-024-00798-1","url":null,"abstract":"<p><strong>Background: </strong>The metabolic response of primary central nervous system lymphoma (PCNSL) patients has yet to be evaluated. This study aimed to assess the prognostic value of a novel scoring scale, the intracranial metabolic score (IMS), in PCNSL patients receiving end-of-therapy ¹⁸F-FDG PET/CT (EOT-PCT) and PET/MR (EOT-PMR).</p><p><strong>Methods: </strong>The IMS was determined based on the metabolism of normal intracranial structures, including gray matter, white matter, and cerebrospinal fluid. The EOT-PCT cohort was evaluated using the IMS and commonly used Deauville score (DS). Another cohort of patients who underwent the EOT-PMR was used to validate the accuracy of the IMS.</p><p><strong>Results: </strong>In total, 83 patients were included in the study (38 in PET/CT cohort, and 45 in PET/MR cohort). The area under the curve (AUC) values of the IMS for predicting PFS and OS were superior to those of the DS. When patients in the PET/CT cohort were stratified into five groups (respectively labeled IMS 1-5), three groups (IMS1-2, IMS 3-4, and IMS 5), or two groups (IMS1-3 and IMS4-5; IMS 1-4 and IMS 5), a higher IMS score was significantly correlated with poorer PFS and OS (p < 0.001). Similar results were observed for PFS in the PET/MR cohort (p < 0.001). The IMS and DS scale were found to be independent prognostic indicators for PFS and OS in the PET/CT cohort, and the IMS was identified as the sole independent prognostic indicator for PFS in the PET/MR cohort.</p><p><strong>Conclusion: </strong>The IMS as a novel and effective prognostic tool for PCNSL patients, showing superior predictive value for patients' outcomes compared to the DS when assessed with EOT-PET scans.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"152"},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615200","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":"Analysis of ultrasound and magnetic resonance imaging characteristics of kaposiform hemangioen dothelioma.","authors":"Chuang Li, Zhimeng Shen, Qi Sun, Gang Wu","doi":"10.1186/s40644-024-00801-9","DOIUrl":"10.1186/s40644-024-00801-9","url":null,"abstract":"<p><strong>Objective: </strong>The present study aims to investigate the ultrasound and magnetic resonance imaging (MRI) characteristics of kaposiform hemangioen dothelioma (KHE).</p><p><strong>Methods: </strong>A retrospective analysis was conducted on the clinical data of children diagnosed with KHE through postoperative pathology. Patients were divided into two groups: the KHE group and the KHE with Kasabach-Merritt Phenomenon (KMP) group (KMP group). Laboratory indicators, ultrasound, and MRI data were collected and analyzed statistically to summarize the imaging characteristics of the disease.</p><p><strong>Results: </strong>The levels of platelets and fibrinogen in the KHE group were significantly higher than those in the KMP group, while D-dimer levels, prothrombin time, and activated partial thromboplastin time were lower (P < 0.05). Ultrasound characteristics comparison revealed that lesions extending to the fat layer (42.47% vs. 54.24%) and invading the muscle layer (38.36% vs. 69.49%) were less common in the KHE group compared to the KMP group, with the lesion diameter being smaller in the KHE group (P < 0.05). The Adler grading predominantly showed Grade II (45.21%) in the KHE group, whereas Grade III (93.22%) was more prevalent in the KMP group (P < 0.05). MRI analysis indicated that the incidence of lesions invading the muscle layer and the presence of flow voids were lower in the KHE group compared to the KMP group (P < 0.05).</p><p><strong>Conclusion: </strong>KHE patients with KMP exhibit lesions that are more prone to extending into the fat layer and invading the muscle layer, with larger diameters and abundant blood flow. Additionally, the MRI images of the lesions may exhibit flow voids.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"150"},"PeriodicalIF":4.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11545901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603087","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":"Multi-parameter MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma.","authors":"Kangjian Hu, Guirong Tan, Xueqing Liao, Weiyin Vivian Liu, Wenjing Han, Lingjing Hu, Haihui Jiang, Lijuan Yang, Ming Guo, Yaohong Deng, Zhihua Meng, Xiang Liu","doi":"10.1186/s40644-024-00796-3","DOIUrl":"10.1186/s40644-024-00796-3","url":null,"abstract":"<p><strong>Background: </strong>Postoperative progressive cerebral edema and hemorrhage (PPCEH) are major complications after meningioma resection, yet their preoperative predictive studies are limited. The aim is to develop and validate a multiparametric MRI machine learning model to predict PPCEH after meningioma resection.</p><p><strong>Methods: </strong>This retrospective study included 148 patients with meningioma. A stratified three-fold cross-validation was used to split the dataset into training and validation sets. Radiomics features from the tumor enhancement (TE) and peritumoral brain edema (PTBE) regions were extracted from T1WI, T2WI, and ADC maps. Support vector machine constructed different radiomics models, and logistic regression explored clinical risk factors. Prediction models, integrating clinical and radiomics features, were evaluated using the area under the curve (AUC), visualized in a nomogram.</p><p><strong>Results: </strong>The radiomics model based on TE and PTBE regions (training set mean AUC: 0.85 (95% CI: 0.78-0.93), validation set mean AUC: 0.77 (95%CI: 0.63-0.90)) outperformed the model with TE region solely (training set mean AUC: 0.83 (95% CI: 0.76-0.91), validation set mean AUC: 0.73 (95% CI: 0.58-0.87)). Furthermore, the combined model incorporating radiomics features, and clinical features of preoperative peritumoral edema and tumor boundary adhesion, had the best predictive performance, with AUC values of 0.87 (95% CI: 0.80-0.94) and 0.84 (95% CI: 0.72-0.95) for the training and validation set.</p><p><strong>Conclusions: </strong>We developed a novel model based on clinical characteristics and multiparametric radiomics features derived from TE and PTBE regions, which can accurately and non-invasively predict PPCEH after meningioma resection. Additionally, our findings suggest the crucial role of PTBE radiomics features in understanding the potential mechanisms of PPCEH.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"149"},"PeriodicalIF":3.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564087","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":"Correction: New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.","authors":"Gagandeep Singh, Annie Singh, Joseph Bae, Sunil Manjila, Vadim Spektor, Prateek Prasanna, Angela Lignelli","doi":"10.1186/s40644-024-00795-4","DOIUrl":"10.1186/s40644-024-00795-4","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"148"},"PeriodicalIF":3.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557252","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":"Role of [¹⁸F]FDG PET/CT in the management of follicular cell-derived thyroid carcinoma.","authors":"Klaudia Zajkowska, Paulina Cegla, Marek Dedecjus","doi":"10.1186/s40644-024-00791-8","DOIUrl":"10.1186/s40644-024-00791-8","url":null,"abstract":"<p><p>Follicular cell-derived thyroid carcinomas constitute the majority of thyroid malignancies. This heterogeneous group of tumours includes well differentiated, poorly differentiated, and undifferentiated forms, which have distinct pathological features, clinical behaviour, and prognosis. Positron emission tomography with 2-[¹⁸F]fluoro-2-deoxy-D-glucose combined with computed tomography ([¹⁸F]FDG PET/CT) is an imaging modality used in routine clinical practice for oncological patients. [¹⁸F]FDG PET/CT has emerged as a valuable tool for identifying patients at high risk of poor clinical outcomes and for facilitating individualized clinical decision-making. The aim of this comprehensive review is to summarize current knowledge regarding the role of [¹⁸F]FDG PET/CT in primary diagnosis, treatment, and follow-up of follicular cell-derived thyroid carcinomas considering the degree of differentiation. Controversial issues, including significance of accidentally detected [¹⁸F]FDG uptake in the thyroid, the role of [¹⁸F]FDG PET/CT in the early assessment of response to molecular targeted therapies, and its prognostic value are discussed in detail.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"147"},"PeriodicalIF":3.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521090","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":"Ultrasound-guided intra-tumoral administration of directly-injected therapies: a review of the technical and logistical considerations.","authors":"George Gabriel Bitar, Melissa Persad, Alina Dragan, Adebayo Alade, Pablo Jiménez-Labaig, Edward Johnston, Samuel J Withey, Nicos Fotiadis, Kevin J Harrington, Derfel Ap Dafydd","doi":"10.1186/s40644-024-00763-y","DOIUrl":"10.1186/s40644-024-00763-y","url":null,"abstract":"<p><strong>Background: </strong>Directly-injected therapies (DIT) include a broad range of agents within a developing research field in cancer immunotherapy, with encouraging clinical trial results in various tumour subtypes. Currently, the majority of such therapies are only available within clinical trials; however, more recently, talimogene laherparepvec (T-VEC, Imlygic) has been approved as the first oncolytic virus therapy in the USA and Europe. Our institution contributes to multiple different trials exploring the efficacy of DIT, the majority of which are performed by oncologists in clinic. However, specific, challenging cases - mainly neck tumours - require image-guided administration.</p><p><strong>Main body: </strong>This review article addresses the technical and logistical factors relevant to the incorporation of image-guided DIT into an established ultrasound service. Image-guidance (usually with ultrasound) is frequently needed for certain targets that cannot be palpated or are in high-risk locations, e.g. adjacent to blood vessels. A multi-disciplinary approach is essential to facilitate a safe and efficient service, including careful case-selection. Certain protocols and guidance need to be followed when incorporating such a service into an established ultrasound practice to enhance efficiency and optimise safety. Key learning points are drawn from the literature and from our early experience at a tertiary cancer centre following image guided DIT for an initial cohort of 22 patients (including 11 with a neck mass), addressing trial protocols, pre-procedure work-up, organisation, planning, consent, technical aspects, procedure tolerability, technical success, and post-procedure considerations.</p><p><strong>Conclusion: </strong>With appropriate planning and coordination, and application of the learning points discussed herein, image-guided administration of DIT can be safely and efficiently incorporated into an established procedural ultrasound list. This has relevance to cancer centres, radiology departments, individual radiologists, and other team members with a future role in meeting the emerging need for these procedures. This paper provides advice on developing such an imaging service, and offers certain insights into the evolving remit of radiologists within cancer care in the near future.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"145"},"PeriodicalIF":3.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495705","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":"Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer.","authors":"Yuting Wu, Jingxu Li, Li Ding, Jianbin Huang, Mingwang Chen, Xiaomei Li, Xiang Qin, Lisheng Huang, Zhao Chen, Yikai Xu, Chenggong Yan","doi":"10.1186/s40644-024-00793-6","DOIUrl":"10.1186/s40644-024-00793-6","url":null,"abstract":"<p><strong>Background: </strong>To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.</p><p><strong>Results: </strong>This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.</p><p><strong>Conclusions: </strong>The combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"146"},"PeriodicalIF":3.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495703","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}