Cancer Imaging最新文献

{"title":"Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study.","authors":"Zhihui Chen, Hongqing Zhu, Hongmin Shu, Jianbo Zhang, Kangchen Gu, Wenjun Yao","doi":"10.1186/s40644-025-00875-z","DOIUrl":"https://doi.org/10.1186/s40644-025-00875-z","url":null,"abstract":"<p><strong>Objectives: </strong>The World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) is crucial for prognosis and treatment planning. This study aims to predict the grade using intratumoral and peritumoral subregional CT radiomics analysis for better clinical interventions.</p><p><strong>Methods: </strong>Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. Using ITK-SNAP, two radiologists annotated tumor regions of interest (ROI) and extended surrounding areas by 1 mm, 3 mm, and 5 mm. The K-means clustering algorithm divided the tumor region into three sub-regions, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the most predictive features. Various machine learning models were established, including radiomics models, peritumoral radiomics models, models based on intratumoral heterogeneity (ITH) score, clinical models, and comprehensive models. Predictive ability was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, DeLong tests, calibration curves, and decision curves.</p><p><strong>Results: </strong>The combined model showed strong predictive power with an AUC of 0.852 (95% CI: 0.725-0.979) on the test data, outperforming individual models. The ITH score model was highly precise, with AUCs of 0.891 (95% CI: 0.854-0.927) in training, 0.877 (95% CI: 0.814-0.941) in validation, and 0.847 (95% CI: 0.725-0.969) in testing, proving its superior predictive ability across datasets.</p><p><strong>Conclusion: </strong>A comprehensive model combining Habitat, Peri1mm, and salient clinical features was significantly more accurate in predicting ccRCC pathologic grading.</p><p><strong>Key points: </strong>Question: Characterize tumor heterogeneity to non-invasively predict WHO/ISUP pathological grading preoperatively.</p><p><strong>Findings: </strong>An integrated model combining subregion characterization, peritumoral characteristics, and clinical features can predict ccRCC grade preoperatively.</p><p><strong>Clinical relevance: </strong>Subregion tumor characterization outperforms the single-entity approach. The integrated model, compared with the radiomics model, boosts grading and prognostic accuracy for more targeted clinical actions.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"59"},"PeriodicalIF":3.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981744","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":"Enhancing ¹⁸F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm.","authors":"Zhihao Chen, Hongxing Yang, Ming Qi, Wen Chen, Fei Liu, Shaoli Song, Jianping Zhang","doi":"10.1186/s40644-025-00877-x","DOIUrl":"https://doi.org/10.1186/s40644-025-00877-x","url":null,"abstract":"<p><strong>Background: </strong>As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to identify methods that ensure consistent diagnostic accuracy and maintain image quality. Deep progressive learning (DPL) algorithm, an Artificial Intelligence(AI)-based PET reconstruction technique, offers a promising solution.</p><p><strong>Methods: </strong>150 patients underwent ¹⁸F-FDG PET/CT scans and were categorized by BMI into underweight, normal, and overweight groups. PET images were reconstructed using both OSEM and DPL and their image quality was assessed both visually and quantitatively. Visual assessment employed a 5-point Likert scale to evaluate overall score, image sharpness, image noise, and diagnostic confidence. Quantitative assessment parameters included the background liver image-uniformity-index ([Formula: see text]) and signal-to-noise ratio ([Formula: see text]). Additionally, 466 identifiable lesions were categorized by size: sub-centimeter and larger. We compared maximum standard uptake value ([Formula: see text]), signal-to-background ratio ([Formula: see text]), [Formula: see text], contrast-to-background ratio ([Formula: see text]), and contrast-to-noise ratio ([Formula: see text]) of these lesions to evaluate the diagnostic performance of the DPL and OSEM algorithms across different lesion sizes and BMI categories.</p><p><strong>Results: </strong>DPL produced superior PET image quality compared to OSEM across all BMI groups. The visual quality of DPL showed a slight decline with increasing BMI, while OSEM exhibited a more significant decline. DPL maintained a stable [Formula: see text] across BMI increases, whereas OSEM exhibited increased noise. In the DPL group, quantitative image quality for overweight patients matched that of normal patients with minimal variance from underweight patients. In contrast, OSEM demonstrated significant declines in quantitative image quality with rising BMI. DPL yielded significantly higher contrast ([Formula: see text], [Formula: see text],[Formula: see text]) and [Formula: see text] than OSEM for all lesions across all BMI categories.</p><p><strong>Conclusion: </strong>DPL consistently provided superior image quality and lesion diagnostic performance compared to OSEM across all BMI categories in ¹⁸F-FDG PET/CT scans. Therefore, we recommend using the DPL algorithm for ¹⁸F-FDG PET/CT image reconstruction in all BMI patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"58"},"PeriodicalIF":3.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981735","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 machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.","authors":"Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang","doi":"10.1186/s40644-025-00876-y","DOIUrl":"https://doi.org/10.1186/s40644-025-00876-y","url":null,"abstract":"<p><strong>Background: </strong>The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN.</p><p><strong>Methods: </strong>We retrospectively collected clinical data from 2,118 patients with left-sided CRC and contrast-enhanced CT images from 310 of these patients. From this data, a test set, a training set, and a temporal validation set were constructed. Logistic regression models were used to develop a clinical model, a CT model, and a radiomics model, which were then integrated into a combined model using logical rules. Finally, these models were evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), precision-recall (PR) curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).</p><p><strong>Results: </strong>A clinical model, a CT model, and a radiomics model were constructed using univariate logistic regression. A combined model was developed by integrating the clinical, CT, and radiomics models, with positivity defined as all three models being positive at a 90% sensitivity threshold. The clinical model included six predictive factors: tumor site, endoscopic obstruction, CEA levels, growth type, differentiation grade, and pathological classification. The CT model utilized largest lymph node average CT value, short-axis diameter and long-axis diameter. The radiomics model incorporated maximum gray level intensity within the region of interest, large area high gray level emphasis, small area high gray level emphasis and surface area to volume ratio. In the test set, the AUCs for the clinical, CT, radiomics, and combined models were 0.694, 0.663, 0.72, and 0.663, respectively, while in the temporal validation set, they were 0.743, 0.629, 0.716, and 0.8. Specifically, the combined model demonstrated a sensitivity of 0.8 and a specificity of 0.8 in the temporal validation set. By comparing the PR and DCA curves, the combined model demonstrated better performance. Additionally, the combined model showed moderate improvements in INR and IDI compared to other models.</p><p><strong>Conclusion: </strong>A clinical and CT-based radiomics model shows promise in predicting No. 253 LN metastasis in left-sided CRC and provides insights for optimizing IMA ligation strategies.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"57"},"PeriodicalIF":3.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12039209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960203","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":"Building a pre-surgical multiparametric-MRI-based morphologic, qualitative, semiquantitative, first and high-order radiomic predictive treatment response model for undifferentiated pleomorphic sarcoma to replace RECIST.","authors":"Raul F Valenzuela, Elvis Duran-Sierra, Mathew Antony, Behrang Amini, Sam Lo, Keila E Torres, Robert S Benjamin, Jingfei Ma, Ken-Pin Hwang, R Jason Stafford, Dejka Araujo, Andrew J Bishop, Ravin Ratan, Wei-Lien Wang, Jossue Espinoza, Pia V Valenzuela, Chengyue Wu, John E Madewell, William A Murphy, Colleen M Costelloe","doi":"10.1186/s40644-025-00873-1","DOIUrl":"https://doi.org/10.1186/s40644-025-00873-1","url":null,"abstract":"<p><strong>Background: </strong>Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft-tissue sarcomas. It demonstrates post-therapeutic hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. Our research aims to establish the multiparametric MRI (mp-MRI) value for predicting UPS treatment response.</p><p><strong>Methods: </strong>An IRB-approved retrospective study included 33 extremity UPS patients with pre-operative mp-MRI, including diffusion-weighted imaging (DWI), contrast-enhanced susceptibility-weighted imaging (CE-SWI), and perfusion-weighted imaging with dynamic contrast-enhancement (PWI/DCE), and surgical resection between February 2021 and May 2023. Lesions were visually classified on CE-SWI into one of 6 morphology patterns. On PWI/DCE, lesions were classified into one of 6 patterns, and time-intensity curves (TICs) were classified as types I-V. Patients were categorized into three groups based on the percentage of pathology-assessed treatment effect (PATE) in the surgical specimen: Responders (> = 90% PATE, n = 16), partial-responders (31-89% PATE, n = 10), and non-responders (< = 30% PATE, n = 7).</p><p><strong>Results: </strong>At post-radiation therapy (PRT), a CE-SWI Complete-Ring pattern was observed in 71% of responders (p = 7.71 × 10^-6). On PWI/DCE images, 79% of responders displayed a Capsular pattern (p = 1.49 × 10^-7), and 100% demonstrated a TIC-type II (p = 8.32 × 10^-7). ROC analysis comparing responders (n = 14) vs. partial/non-responders (n = 16) at PRT showed that the model combining PWI/DCE TIC-type II, PWI/DCE Capsular pattern, and CE-SWI Complete-Ring pattern yielded the highest classification performance (AUC = 0.99), outperforming PWI/DCE Capsular + TIC-type II (AUC = 0.97), PWI/DCE Capsular (AUC = 0.89), PWI/DCE TIC-type II (AUC = 0.88), and CE-SWI Complete Ring (AUC = 0.79). Contrary to prior reports, DWI/ADC played a secondary role in predicting response: ADC mean & skewness (AUC = 0.63). RECIST demonstrated 100% stability at PRT and 100% pseudo-progression at PC in responders and partial/non-responders (AUC = 0.47).</p><p><strong>Conclusion: </strong>Mp-MRI-derived features are valuable in assessing UPS treatment response. A pre-operative model that combines PWI/DCE TIC-type II, PWI/DCE Capsular pattern, and CE-SWI Complete Ring pattern can reliably predict successfully treated UPS with > = 90% PATE, outperforming RECIST, which was proven unreliable in separating responders from partial/non-responders. Institutions that have not yet implemented CE-SWI can rely on a single-sequence approach based on PWI/DCE, combining the presence of TIC II and Capsular enhancement as criteria for response prediction.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"56"},"PeriodicalIF":3.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954009","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":"Evaluation of 3D ARFI imaging of prostate cancer: diagnostic reliability and concordance with MpMRI.","authors":"Spencer R Moavenzadeh, Derek Y Chan, Eric S Adams, Sriram Deivasigamani, Srinath Kotamarti, Mark L Palmeri, Thomas J Polascik, Kathryn R Nightingale","doi":"10.1186/s40644-025-00874-0","DOIUrl":"https://doi.org/10.1186/s40644-025-00874-0","url":null,"abstract":"<p><strong>Purpose: </strong>The prevalence of prostate cancer (PCa) necessitates advanced diagnostic approaches for detection and lesion characterization. Utilizing two patient cohorts (n = 85), this study analyzes a custom-designed 3D ultrasonic acoustic radiation force impulse (ARFI) elasticity imaging system alongside an Index of Suspicion (IOS) lesion ranking system to evaluate reader sensitivity, positive predictive values, inter-reader reliability, and ARFI-mpMRI concordance. The IOS system provides standardized criteria for lesion assessment, enabling consistency in stratifying PCa lesion suspicion.</p><p><strong>Materials and methods: </strong>Three readers were trained on multiparametric ultrasound (mpUS) (combined ARFI and B-mode) prostate image volumes from 6 patients based on the IOS criteria. The readers then marked suspicious lesions in 79 patients who were retrospectively compared with histopathology-identified (Cohort I, post-radical prostatectomy) or biopsy-confirmed (Cohort II) cancerous regions.</p><p><strong>Results: </strong>The IOS criteria stratified lesions by Gleason grade (GG), with a higher IOS correlating with more aggressive lesions. mpUS imaging was more sensitive for detecting lesions with higher GG and preferentially identified lesions with lower MR apparent-diffusion coefficients and signs of extraprostatic extension. mpUS imaging demonstrated substantial inter-reader reliability and moderate overlap with mpMRI lesions, with increasing sensitivity to higher MRI PI-RADS score lesions. mpUS imaging was less sensitive than mpMRI to lesions with lower GG.</p><p><strong>Conclusions: </strong>The increased sensitivity of mpUS imaging to higher GG lesions and adverse histopathological factors, along with moderate agreement with mpMRI, suggest that mpUS has the potential to guide biopsy targeting of mpMRI-visible lesions or serve as an alternative biopsy-targeting approach when mpMRI is unavailable or clinically contraindicated.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"55"},"PeriodicalIF":3.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970859","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":"Cabazitaxel-induced ureteritis in metastatic castration-resistant prostate cancer patients: a single center case series 2014-2024.","authors":"Ahmed H Zedan, Jesper S Gade, Karsten Egbert Arnold Zieger, Mads H Poulsen, Anja Schmidt Vejlgaard, Filip Lund Hjorth Fredensborg","doi":"10.1186/s40644-025-00872-2","DOIUrl":"https://doi.org/10.1186/s40644-025-00872-2","url":null,"abstract":"<p><strong>Background: </strong>One of the main and effective therapy choices for patients with metastatic castration-resistant prostate cancer (mCRPC) is cabazitaxel (CBZ). Cystitis and hematuria are among the most significant non-hematological adverse events associated with CBZ treatment. But because the prevalence of CBZ-induced ureteritis has not been thoroughly studied, this case series investigation was carried out to emphasize the condition's clinical relevance and potential treatment alternatives.</p><p><strong>Case presentation: </strong>Between June 2014 and May 2024, 354 patients diagnosed with mCRPC were treated with CBZ at the Department of Oncology, Vejle Hospital. A total of 36 patients (10%) exhibited ureteritis-like symptoms, presenting with discomfort in the pelvis, lower abdomen, or flanks, with or without hematuria. Radiological evidence of ureter changes was present in 29 out of 36 individuals (80%), along with hydronephrosis/hydroureter in some patients. Prior to therapy with CBZ, radiation to the pelvis or lower abdomen was documented in 7 out of 36 patients (19%). Various analgesics and dosage modifications were considered for the therapy of CBZ-induced ureteritis, with treatment discontinuation yielding the most favorable results.</p><p><strong>Conclusion: </strong>The onset of ureteritis during CBZ treatment is an underrated side effect in clinical practice. Hematuria and hydronephrosis/hydroureter are the most associated complications. Both analgesics and dosage reduction should be contemplated for management, while therapy cessation may be requisite in certain individuals.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"54"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980352","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":"Evaluation of CE-MATRIX-Enhanced FLAIR imaging in the detection of leptomeningeal metastasis.","authors":"Junhui Yuan, Shaobo Fang, Fan Meng, Yue Wu, Dongqiu Shan, Chunmiao Xu, Renzhi Zhang, Xuejun Chen","doi":"10.1186/s40644-025-00867-z","DOIUrl":"10.1186/s40644-025-00867-z","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the diagnostic value of CE-MATRIX-T1FLAIR and 3D CE-T2FLAIR sequences based on Contrast Enhancement Modulated flip Angle Technique in Refocused Imaging with eXtended echo train (CE-MATRIX) technology for detecting Leptomeningeal Metastasis (LM) using Fluid Attenuated Inversion Recovery (FLAIR) imaging.</p><p><strong>Methods: </strong>This prospective study included 563 hospitalized patients with clinically suspected LM, diagnosed with malignant tumors between January 2022 and October 2023 at Henan Cancer Hospital. Both CE-MATRIX-T1FLAIR and 3D CE-T2FLAIR sequences were used for imaging. Two radiologists independently evaluated image quality, diagnostic confidence, and objective measurements, diagnosing LM as positive or negative, with disagreements resolved by consultation. Subjective and objective scores were compared using the Wilcoxon signed-rank test. The diagnostic performance of the sequences was compared using ROC curve analysis, with cerebrospinal fluid (CSF) cytology as the gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) values were calculated and compared using Z-tests.</p><p><strong>Results: </strong>LM was confirmed in 321 patients. CE-MATRIX-T1FLAIR showed superior subjective scores in image quality and diagnostic confidence (p < 0.001). Though CE-MATRIX-T1FLAIR had a lower SNR (p = 0.013), it demonstrated higher sensitivity, specificity, PPV, NPV, accuracy, and AUC than 3D CE-T2FLAIR (p < 0.001). Both sequences provided effective diagnosis and differentiation of LM.</p><p><strong>Conclusion: </strong>CE-MATRIX-T1FLAIR offers superior diagnostic performance compared to 3D CE-T2FLAIR for LM, with slightly better subjective ratings despite a lower SNR. Both sequences are effective for diagnosing LM.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"53"},"PeriodicalIF":3.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810541","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":"Glymphatic system dysfunction and cerebrospinal fluid retention in gliomas: evidence from perivascular space diffusion and volumetric analysis.","authors":"Weiqiang Liang, Wenbo Sun, Chunyan Li, Jie Zhou, Changyou Long, Huan Li, Dan Xu, Haibo Xu","doi":"10.1186/s40644-025-00868-y","DOIUrl":"10.1186/s40644-025-00868-y","url":null,"abstract":"<p><strong>Background: </strong>Gliomas may impair glymphatic function and alter cerebrospinal fluid (CSF) dynamics through structural brain changes, potentially affecting peritumoral brain edema (PTBE) and fluid clearance. This study investigated the impact of gliomas on glymphatic system function and CSF volume via diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) and volumetric magnetic resonance imaging (MRI), which clarified the relationships between tumor characteristics and glymphatic system disruption.</p><p><strong>Methods: </strong>In this prospective study, 112 glioma patients and 56 healthy controls underwent MRI to calculate DTI-ALPS indices and perform volumetric analyses of CSF, tumor, and PTBE. Statistical analyses were used to assess the relationships between the DTI-ALPS index, tumor volume, PTBE volume, and clinical characteristics.</p><p><strong>Results: </strong>Glioma patients had significantly lower DTI-ALPS indices (1.266 ± 0.258 vs. 1.395 ± 0.174, p < 0.001) and greater CSF volumes (174.53 ± 34.89 cm³ vs. 154.25 ± 20.89 cm³, p < 0.001) than controls did. The DTI-ALPS index was inversely correlated with tumor volume (r = -0.353, p < 0.001) and PTBE volume (r = -0.266, p = 0.015). High-grade gliomas were associated with lower DTI-ALPS indices and larger PTBE volumes (all p < 0.001). Tumor grade emerged as an independent predictor of the DTI-ALPS index in multivariate analysis (β = -0.244, p = 0.011).</p><p><strong>Conclusion: </strong>Gliomas are associated with significant glymphatic dysfunction, as evidenced by reduced DTI-ALPS indices and increased CSF and PTBE volumes. The DTI-ALPS index serves as a potential biomarker of glymphatic disruption in glioma patients, offering insights into tumor-related fluid changes and the pathophysiology of brain-tumor interactions.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"51"},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802612","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":"PET/MRI is superior to PET/CT in detecting oesophago and gastric carcinomas: a meta-analysis.","authors":"Bo Peng, Hui Sun, Jian Hou, Jian-Xing Luo","doi":"10.1186/s40644-025-00871-3","DOIUrl":"10.1186/s40644-025-00871-3","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the accuracy rates of the detection and staging of oesophago and gastric carcinomas between PET/MRI and PET/CT.</p><p><strong>Methods: </strong>An extensive librarian-led literature search of PubMed, Embase, Web of Science, the Cochrane Central Library, and CNKI was performed and a meta-analysis was done.</p><p><strong>Results: </strong>Six studies, including 123 participants, were analyzed. PET/MRI had a comparatively high sensitivity in primary lesion detection compared with PET/CT. (RR = 1.14, 95% CI 1.01-1.29, P = 0.036).PET/MRI had no significant statistical differences in all aspects of TNM staging compared with PET/CT.</p><p><strong>Conclusions: </strong>This systematic review confirmed the advantage of PET/MRI in detecting oesophago and gastric carcinomas.Compared with PET/CT, it can reduce unnecessary radiation exposure and can be used in relevant patients without contraindications of MRI.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"50"},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802614","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: Total lesion Glycolysis of primary tumor and lymphnodes is a strong predictor for development of distant metastases in oropharyngeal carcinoma patients with independent validation in automatically delineated lesions.","authors":"Sebastian Zschaeck, Marina Hajiyianni, Patrick Hausmann, Pavel Nikulin, Emily Kukuk, Christian Furth, Paulina Cegla, Elia Lombardo, Joanna Kazmierska, Adrien Holzgreve, Iosif Strouthos, Carmen Stromberger, Claus Belka, Michael Baumann, Mechthild Krause, Guillaume Landry, Witold Cholewinski, Jorg Kotzerke, Daniel Zips, Jörg van den Hoff, Frank Hofheinz","doi":"10.1186/s40644-025-00870-4","DOIUrl":"10.1186/s40644-025-00870-4","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"52"},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802610","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}