Cancer Imaging最新文献

{"title":"Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT.","authors":"Xianwang Liu, Tao Han, Yuzhu Wang, Hong Liu, Juan Deng, Caiqiang Xue, Shenglin Li, Junlin Zhou","doi":"10.1186/s40644-024-00820-6","DOIUrl":"10.1186/s40644-024-00820-6","url":null,"abstract":"<p><strong>Purpose: </strong>To assess and compare the diagnostic efficiency of histogram analysis of monochromatic and iodine images derived from spectral CT in predicting Ki-67 expression in gastric gastrointestinal stromal tumors (gGIST).</p><p><strong>Methods: </strong>Sixty-five patients with gGIST who underwent spectral CT were divided into a low-level Ki-67 expression group (LEG, Ki-67 < 10%, n = 33) and a high-level Ki-67 expression group (HEG, Ki-67 ≥ 10%, n = 32). Conventional CT features were extracted and compared. Histogram parameters were extracted from monochromatic and iodine images, respectively. The diagnostic efficiency of the histogram parameters from monochromatic and iodine images was assessed and compared between the two groups. Spearman's correlation analysis was used to correlate histogram parameters with Ki-67 expression.</p><p><strong>Results: </strong>The HEG was more likely to present with an irregular shape and a larger size than the LEG (all p < 0.05). Regarding histogram parameters, the HEG showed higher maximum, mean, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.99, SD, variance, and CV of monochromatic images; higher maximum, Perc.99, and entropy of iodine images, compared with the LEG (all p < 0.003125). ROC analysis showed that significant histogram parameters of monochromatic and iodine images allowed for effective differentiation between LEG and HEG. DeLong's test showed that the diagnostic efficiency of histogram parameters in monochromatic images (Perc.90) was superior to that of iodine images (maximum) (p = 0.010). A positive correlation was observed between the significant histogram parameters and Ki-67 expression (all p < 0.05).</p><p><strong>Conclusion: </strong>Both histogram analysis of monochromatic and iodine images derived from spectral CT can predict Ki-67 expression in gGIST, and the diagnostic efficacy of monochromatic images is superior to iodine images.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"173"},"PeriodicalIF":3.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11686923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909463","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":"Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions.","authors":"Xuan Yu, Jing Zhou, Yaping Wu, Yan Bai, Nan Meng, Qingxia Wu, Shuting Jin, Huanhuan Liu, Panlong Li, Meiyun Wang","doi":"10.1186/s40644-024-00817-1","DOIUrl":"10.1186/s40644-024-00817-1","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients.</p><p><strong>Methods: </strong>Clinical, pathological, and MRI data of 356 glioblastoma patients (251 methylated, 105 unmethylated) were retrospectively examined from the public dataset The Cancer Imaging Archive. Each patient underwent preoperative multi-sequence brain MRI scans, which included T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Regions of interest (ROIs) were delineated to identify the necrotic tumor core (NCR), enhancing tumor (ET), and peritumoral edema (PED). The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance.</p><p><strong>Results: </strong>The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 - 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively.</p><p><strong>Conclusion: </strong>The deep learning model based on MRI data can effectively distinguish between glioblastoma patients with and without MGMT promoter methylation.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"172"},"PeriodicalIF":3.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881135","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":"Prognostic value of metabolic tumor volume on [¹⁸F]FDG PET/CT in addition to the TNM classification system of locally advanced non-small cell lung cancer.","authors":"Alexander Brose, Isabelle Miederer, Jochem König, Eleni Gkika, Jörg Sahlmann, Tanja Schimek-Jasch, Mathias Schreckenberger, Ursula Nestle, Jutta Kappes, Matthias Miederer","doi":"10.1186/s40644-024-00811-7","DOIUrl":"10.1186/s40644-024-00811-7","url":null,"abstract":"<p><strong>Purpose: </strong>Staging of non-small cell lung cancer (NSCLC) is commonly based on [¹⁸F]FDG PET/CT, in particular to exclude distant metastases and guide local therapy approaches like resection and radiotherapy. Although it is hoped that PET/CT will increase the value of primary staging compared to conventional imaging, it is generally limited to the characterization of TNM. The first aim of this study was to evaluate the PET parameter metabolic tumor volume (MTV) above liver background uptake as a prognostic marker in lung cancer. The second aim was to investigate the possibility of incorporating MTV into the TNM classification system for disease prognosis in locally advanced NSCLC treated with chemoradiotherapy.</p><p><strong>Methods: </strong>Retrospective evaluation of 235 patients with histologically proven, locally advanced NSCLC from the multi-centre randomized clinical PETPLAN trial and a clinical cohort from a hospital registry. The PET parameters SUVmax, SULpeak, MTV and TLG above liver background uptake were determined. Kaplan-Meier curves and stratified Cox proportional hazard regression models were used to investigate the prognostic value of PET parameters and TNM along with clinical variables. Subgroup analyses were performed to compare hazard ratios according to TNM, MTV, and the two variables combined.</p><p><strong>Results: </strong>In the multivariable Cox regression analysis, MTV was associated with significantly worse overall survival independent of stage and other prognostic variables. In locally advanced disease stages treated with chemoradiotherapy, higher MTV was significantly associated with worse survival (median 17 vs. 32 months). Using simple cut-off values (45 ml for stage IIIa, 48 ml for stage IIIb, and 105 ml for stage IIIc), MTV was able to further predict differences in survival for stages IIIa-c. The combination of TNM and MTV staging system showed better discrimination for overall survival in locally advanced disease stages, compared to TNM alone.</p><p><strong>Conclusion: </strong>Higher metabolic tumor volume is significantly associated with worse overall survival and combined with TNM staging, it provides more precise information about the disease prognosis in locally advanced NSCLC treated with chemoradiotherapy compared to TNM alone. As a PET parameter with volumetric information, MTV represents a useful addition to TNM.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"171"},"PeriodicalIF":3.5,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871515","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":"Identification of D842V mutation in gastrointestinal stromal tumors based on CT radiomics: a multi-center study.","authors":"Zhenhui Xie, Qingwei Zhang, Ranying Zhang, Yuxuan Zhao, Wang Zhang, Yang Song, Dexin Yu, Jiang Lin, Xiaobo Li, Shiteng Suo, Yan Zhou","doi":"10.1186/s40644-024-00815-3","DOIUrl":"10.1186/s40644-024-00815-3","url":null,"abstract":"<p><strong>Background: </strong>Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Recent advent of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of GIST patients. However, responses to TKI therapy can vary depending on the specific gene mutation. D842V, which is the most common mutation in platelet-derived growth factor receptor alpha exon 18, shows no response to imatinib and sunitinib. Radiomics features based on venous-phase contrast-enhanced computed tomography (CECT) have shown potential in non-invasive prediction of GIST genotypes. This study sought to determine whether radiomics features could help distinguish GISTs with D842V mutations.</p><p><strong>Methods: </strong>A total of 872 pathologically confirmed GIST patients with CECT data available from three independent centers were included and divided into the training cohort ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>487</mn></mrow> </math> ) and the external validation cohort ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>385</mn></mrow> </math> ). Clinical features including age, sex, tumor size and location were collected. Radiomics features on the largest axial image of venous-phase CECT were analyzed and a total of two radiomics features were selected after feature selection. Random forest models trained on non-radiomics features only (the non-radiomics model) and on both non-radiomics and radiomics features (the combined model) were compared.</p><p><strong>Results: </strong>The combined model showed better average precision (0.250 vs. 0.102, p = 0.039) and F1 score (0.253 vs. 0.155, p = 0.012) than the non-radiomics model. There was no significant difference in ROC-AUC (0.728 vs. 0.737, p = 0.836) and geometric mean (0.737 vs. 0.681, p = 0.352).</p><p><strong>Conclusions: </strong>This study demonstrated the potential of radiomics features based on venous-phase CECT images to identify D842V mutation in GISTs. Our model may provide an alternative approach to guide TKI therapy for patients inaccessible to sequence variant testing, potentially improving treatment outcomes for GIST patients especially in resource-limited settings.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"169"},"PeriodicalIF":3.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871503","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":"Associations between ADC histogram analysis values and tumor-micro milieu in uterine cervical cancer.","authors":"Alexey Surov, Jan Borggrefe, Anne-Kathrin Höhn, Hans-Jonas Meyer","doi":"10.1186/s40644-024-00814-4","DOIUrl":"10.1186/s40644-024-00814-4","url":null,"abstract":"<p><strong>Background: </strong>The complex interactions of the tumor micromilieu may be reflected by diffusion-weighted imaging (DWI) derived from the magnetic resonance imaging (MRI). The present study investigated the association between apparent diffusion coefficient (ADC) values and histopathologic features in uterine cervical cancer.</p><p><strong>Methods: </strong>In this retrospective study, prebiopsy MRI was used to analyze histogram ADC-parameters. The biopsy specimens were stained for Ki-67, E-cadherin, vimentin and tumor-infiltrating lymphocytes (TIL, all CD45 positive cells). Tumor-stroma ratio (TSR) was calculated on routine H&E specimens. Spearman's correlation analysis and receiver-operating characteristics curves were used as statistical analyses.</p><p><strong>Results: </strong>The patient sample comprised 70 female patients (age range 32-79 years; mean age 55.4 years) with squamous cell cervical carcinoma. The interreader agreement was high ranging from intraclass coefficient (ICC) = 0.71 for entropy to ICC = 0.96 for ADCmedian. Several ADC-histogram parameters correlated strongly with the TSR. The highest correlation coefficient achieved p10 (r = -0.81, p < 0.0001). ADCmean can predict tumors with high TSR, AUC: 0.91, sensitivity: 0.91 (95% CI 0.77;0.96), specificity: 0.91 (95% CI 0.78;0.97). Several ADC-histogram parameters correlated slightly with the proliferation index Ki-67. No associations were found with TIL, E-Cadherin and vimentin. In well and moderately differentiated cancers, ADC histogram values showed stronger correlations with Ki-67 and TSR than in poorly differentiated tumors.</p><p><strong>Conclusion: </strong>ADC values are strongly associated with tumor-stroma ratio. The ADC mean can be used to predict tumors with high TSR. Associations between histopathology and ADC values depend on tumor differentiation. ADC values show only weak associations with Ki-67 and none with TIL, vimentin and E-cadherin.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"170"},"PeriodicalIF":3.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871500","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 intravoxel incoherent motion diffusion weighted imaging-based model for preoperative distinguishing nuclear grade and survival of clear cell renal cell carcinoma complicated with venous tumor thrombus.","authors":"Jian Zhao, Honghao Xu, Yonggui Fu, Xiaohui Ding, Meifeng Wang, Cheng Peng, Huanhuan Kang, Huiping Guo, Xu Bai, Shaopeng Zhou, Kan Liu, Lin Li, Xu Zhang, Xin Ma, Xinjiang Wang, Haiyi Wang","doi":"10.1186/s40644-024-00816-2","DOIUrl":"10.1186/s40644-024-00816-2","url":null,"abstract":"<p><strong>Objective: </strong>To assess the utility of multiparametric MRI and clinical indicators in distinguishing nuclear grade and survival of clear cell renal cell carcinoma (ccRCC) complicated with venous tumor thrombus (VTT).</p><p><strong>Materials and methods: </strong>This study included 105 and 27 patients in the training and test sets, respectively. Preoperative MRI, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), was performed. Renal lesions were evaluated for IVIM-DWI metrics and conventional MRI features. All the patients had postoperative histologically proven ccRCC and VTT. An expert uropathologist reviewed all specimens to confirm the nuclear grade of the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) of the tumor. Univariate and multivariable logistic regression analyses were used to select the preoperative imaging features and clinical indicators. The predictive ability of the logistic regression model was assessed using receiver operating characteristic (ROC) analysis. Survival curves were plotted using the Kaplan-Meier method.</p><p><strong>Results: </strong>High WHO/ISUP nuclear grade was confirmed in 69 of 105 patients (65.7%) in the training set and 19 of 27 patients (70.4%) in the test set, respectively (P = 0.647). D_{p_ROI_Low}, tumor size, serum albumin, platelet count, and lymphocyte count were independently related to high WHO/ISUP nuclear grade in the training set. The model identified high WHO/ISUP nuclear grade well, with an AUC of 0.817 (95% confidence interval [CI]: 0.735-0.899), a sensitivity of 70.0%, and a specificity of 77.8% in the training set. In the independent test set, the model demonstrated an AUC of 0.766 (95% CI, 0.567-0.966), a sensitivity of 79.0%, and a specificity of 75.0%. Kaplan-Meier analysis showed that the predicted high WHO/ISUP nuclear grade group had poorer progression-free survival than the low WHO/ISUP nuclear grade group in both the training and test sets (P = 0.001 and P = 0.021).</p><p><strong>Conclusions: </strong>IVIM-DWI-derived parameters and clinical indicators can be used to differentiate nuclear grades and predict progression-free survival of ccRCC and VTT.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"164"},"PeriodicalIF":3.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852889","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":"Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.","authors":"Sung-Hua Chiu, Hsiao-Chi Li, Wei-Chou Chang, Chao-Cheng Wu, Hsuan-Hwai Lin, Cheng-Hsiang Lo, Ping-Ying Chang","doi":"10.1186/s40644-024-00809-1","DOIUrl":"10.1186/s40644-024-00809-1","url":null,"abstract":"<p><strong>Background: </strong>To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bevacizumab-based chemotherapy (BBC).</p><p><strong>Methods: </strong>A retrospective review of liver-only mCRC patients treated with BBC from December 2011 to Apr 2021 was performed. Patients who had metachronous liver metastases or received locoregional treatment before the initiation of BBC were excluded. The percentage of downstaging to curative treatment and overall survival (OS) were recorded. Two abdominal radiologists evaluated portal venous phase CT images based on the morphological criteria and divided the images into Groups 1, 2, and 3. These images were used to establish the radiologists-determined morphological response (RD-MR), which classified patients into responders and non-responders based on the morphological change 3 months after the initiation of BBC. Then, the Group 1 and 3 images classified by the radiologists were input into ResNet as the training dataset. The trained ResNet then redivided the Group 2 images into Groups 1, 2 and 3. The ResNet-MR was determined on the basis of these redivided images and the initial Group 1 and 3 images classified by the radiologists.</p><p><strong>Results: </strong>Eighty-four patients were included in this study (53 males and 31 females, with a median age of 60.0 years). The follow-up time ranged from 10 to 86 months. A total of 407 CT images were input into ResNet as the training dataset. Both RD-MR and ResNet-MR correlated with OS (p value = 0.0167 and 0.0225, respectively). Regarding discriminatory ability for mortality, ResNet-MR had higher area under curve than RD-MR at both 1 year and 2 years and showed a significant difference in discriminatory ability (p-value = 0.0321) at 2 years. RD-MR classified 28 patients (33.3%) as responders, and ResNet-MR classified an additional 16 patients (19.0%) as responders; these 16 patients had longer OS than the remaining non-responders in the RD-MR group (27.49 versus 21.20 months, p value = 0.043) and had a higher percentage of downstaging (37.5% versus 17.5%, p value = 0.1610).</p><p><strong>Conclusions: </strong>In CRC patients with liver metastases treated with BBC, prediction of survival can be improved with the aid of ResNet, enabling optimized individualized treatment.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"165"},"PeriodicalIF":3.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852902","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 assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.","authors":"Xiaojiang Zhao, Yuhang Wang, Mengli Xue, Yun Ding, Han Zhang, Kai Wang, Jie Ren, Xin Li, Meilin Xu, Jun Lv, Zixiao Wang, Daqiang Sun","doi":"10.1186/s40644-024-00813-5","DOIUrl":"10.1186/s40644-024-00813-5","url":null,"abstract":"<p><strong>Objective: </strong>To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment.</p><p><strong>Methods: </strong>The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm, 6 mm, and 8 mm, respectively, and deep learning image features were extracted through a convolutional neural network. Subsequently, the RAITS was constructed. The performance of RAITS was then evaluated in both the train and validation cohorts.</p><p><strong>Results: </strong>RAITS demonstrated superior AUC, sensitivity, and specificity in both the training and external validation cohorts, outperforming traditional unimodal models. In the validation cohort, RAITS achieved an AUC of 0.78 (95% CI, 0.69-0.88) and showed higher net benefits across most threshold ranges. RAITS exhibited strong discriminative ability in risk stratification, with p < 0.01 in the training cohort and p = 0.02 in the validation cohort, consistent with the actual predictive performance of TLSs, where TLS-positive patients had significantly higher recurrence-free survival (RFS) compared to TLS-negative patients (p = 0.04 in the training cohort, p = 0.02 in the validation cohort).</p><p><strong>Conclusion: </strong>As a multimodal predictive model based on preoperative CT radiomic features, RAITS demonstrated excellent performance in identifying TLSs in stage I LUAD and holds potential value in clinical decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"167"},"PeriodicalIF":3.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852907","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":"Clinical scoring systems, molecular subtypes and baseline [¹⁸F]FDG PET/CT image analysis for prognosis of diffuse large B-cell lymphoma.","authors":"Zhuxu Sun, Tianshuo Yang, Chongyang Ding, Yuye Shi, Luyi Cheng, Qingshen Jia, Weijing Tao","doi":"10.1186/s40644-024-00810-8","DOIUrl":"10.1186/s40644-024-00810-8","url":null,"abstract":"<p><p>Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous hematological malignancy resulting in a range of outcomes, and the early prediction of these outcomes has important implications for patient management. Clinical scoring systems provide the most commonly used prognostic evaluation criteria, and the value of genetic testing has also been confirmed by in-depth research on molecular typing. [¹⁸F]-fluorodeoxyglucose positron emission tomography / computed tomography ([¹⁸F]FDG PET/CT) is an invaluable tool for predicting DLBCL progression. Conventional baseline image-based parameters and machine learning models have been used in prognostic FDG PET/CT studies of DLBCL; however, numerous studies have shown that combinations of baseline clinical scoring systems, molecular subtypes, and parameters and models based on baseline FDG PET/CT image may provide better predictions of patient outcomes and aid clinical decision-making in patients with DLBCL.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"168"},"PeriodicalIF":3.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11656546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853201","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":"Comparative analysis of metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using ¹⁸F-FDG PET/CT imaging.","authors":"Yuan Gao, Lei Yin, Linlin Ma, Caixia Wu, Xiaojuan Zhu, Hongjin Liu, Li Liang, Jinzhi Chen, Yulong Chen, Jingming Ye, Ling Xu, Meng Liu","doi":"10.1186/s40644-024-00812-6","DOIUrl":"10.1186/s40644-024-00812-6","url":null,"abstract":"<p><strong>Background: </strong>Recent advancements in novel anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugates (ADCs) have highlighted the emerging HER2-low breast cancer subtype with promising therapeutic efficacy. This study aimed to comparatively analyze the metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using baseline fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) imaging.</p><p><strong>Methods: </strong>Consecutive patients with newly diagnosed breast cancer who underwent ¹⁸F-FDG PET/CT prior to therapy in our hospital were retrospectively reviewed. The relationship between metabolic parameters (maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)) in primary lesions and HER2 expression was analyzed. The survival analyses were performed to identify the prognostic factors for disease-free survival (DFS) in patients with HER2-negative (HER2-low versus -zero).</p><p><strong>Results: </strong>In total, 258 patients (mean age: 54 ± 12 years) were included. In hormone receptor (HR)-positive subgroup, SUVmax and TLR were significantly higher in HER2-low than in HER2-zero (P = 0.045 and 0.03, respectively). But in HR-negative subgroup, there was no significant metabolic difference between HER2-low and HER2-zero (All P > 0.05). The four metabolic parameters were significant predictors of DFS in HER2-negative patients (All P < 0.01), but there was no significant difference in DFS between HER2-low and -zero, regardless of tumor metabolism. Moreover, in HER2-zero patients, the DFS of patients with high metabolism was significantly shorter than that of patients with low metabolism (P_SUVmax = 0.002, P_MTV = 0.03, P_TLG= 0.005, P_TLR < 0.001, respectively), but without a similar finding in HER2-low patients.</p><p><strong>Conclusion: </strong>Our study demonstrated the HR-positive HER2-low breast cancer exhibited a particularity in glucose metabolic profile. Additionally, HER2-zero patients with elevated metabolism were associated with inferior prognosis and warranted careful attention in clinical evaluations.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"166"},"PeriodicalIF":3.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851900","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}