Cancer Imaging最新文献

Background: The incidence of papillary thyroid carcinoma (PTC) has been increasing, and thermal ablation has emerged as a minimally invasive alternative to surgery for low-risk cases. However, post-ablation tumor progression remains a significant clinical challenge.

Methods: This review synthesizes existing literature on tumor progression after thermal ablation for PTC, analyzing potential causes and evaluating preventive strategies at different diagnostic and treatment stages.

Results: Current research reports indicate that the probability of disease progression following thermal ablation for PTMC ranges from 1.25 to 7.7%, a rate comparable to that of surgical management. Nodules exceeding 10 mm in diameter are associated with a higher risk of post-procedural progression. However, pathological evidence supporting these findings remains limited. Risk factors such as suboptimal patient selection and tumor proximity to critical structures further influence outcomes. Improved imaging guidance, standardized protocols, and stringent follow-up may reduce these complications.

Conclusion: When these recommendations are followed, thermal ablation for PTMC achieves effective reduction in tumor progression risk and represents a viable alternative for appropriately selected patients. However, expansion of its indications requires further robust evidence from large-scale, pathology-based studies.

Objectives: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative gadobenate-enhanced (Gd-BOPTA) magnetic resonance imaging (MRI) using multilayer perceptron (MLP) deep learning to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients.

Methods: A total of 230 patients with histopathologically confirmed HCC who underwent preoperative Gd-BOPTA MRI before hepatectomy were retrospectively enrolled from three hospitals (144, 54, and 32 in training, test, and validation set, respectively). Univariate and multivariate logistic regression analyses were used to determine independent clinicoradiological predictors significantly associated with VETC, which then constituted the clinicoradiological model. Regions of interest (ROIs) included four modes, intratumoral (Tumor), peritumoral area ≤ 2 mm (Peri2mm), intratumoral + peritumoral area ≤ 2 mm (Tumor + Peri2mm) and intratumoral integrated with peritumoral ≤ 2 mm as a whole (TumorPeri2mm). A total of 7322 radiomics features were extracted respectively for ROI(Tumor), ROI(Peri2mm), ROI(TumorPeri2mm) and 14644 radiomics features for ROI(Tumor + Peri2mm). Least absolute shrinkage and selection operator (LASSO) and univariate logistic regression analysis were used to select the important features. Seven different machine learning classifiers respectively combined the radiomics signatures selected from four ROIs to constitute different models, and compare the performance between them in three sets and then select the optimal combination to become the radiomics model we need. Then a radiomics score (rad-score) was generated, which combined significant clinicoradiological predictors to constituted the fusion model through multivariate logistic regression analysis. After comparing the performance of the three models using area under receiver operating characteristic curve (AUC), integrated discrimination index (IDI) and net reclassification index (NRI), choose the optimal predictive model for VETC prediction.

Result: Arterial peritumoral enhancement and peritumoral hypointensity on hepatobiliary phase (HBP) were independent risk factors for VETC, and constituted the Radiology model, without any clinical variables. Arterial peritumoral enhancement defined as the enhancement outside the tumor boundary in the late stage of arterial phase or early stage of portal phase, extensive contact with the tumor edge, which becomes isointense during the DP. MLP deep learning algorithm integrated radiomics features selected from ROI TumorPeri2mm was the best combination, which constituted the radiomics model (MLP model). A MLP score (MLP_score) was calculated then, which combining the two radiology features composed the fusion model (Radiology MLP model), with AUCs of 0.871, 0.894, 0.918 in the training, test and validation sets. Compared with

Background: Potential rural-urban differences in prostate cancer care are understudied, particularly regarding the utilization of advanced diagnostic tests. Herein we examined variations in Positron Emission Tomography (PET) utilization for prostate cancer care, including diagnosis, staging and treatment planning, across residential regions in the United States.

Methods: Patients newly diagnosed with prostate cancer between 2019 and 2021 and post-diagnostic PETs were identified using full Medicare claims data. PET use was assessed in all newly diagnosed patients, though indications vary by risk. Patients' counties were categorized as metro, urban, or rural, from most to least urbanized. Regional PET utilization was further examined at the level of hospital referral regions. A multivariable logistic regression model was performed to assess the impact of rurality on PET imaging. A secondary analysis included an interaction term for race to explore the effect of residence on PET imaging by racial group.

Results: Overall, 495 865 patients were included in the analysis: 393 861 (79.4%) lived in metro, 56 698 (11.4%) in urban and 39 707 (8.0%) in rural counties. Patients in metro counties underwent PET imaging more often (8.4%) than patients in urban (7.3%) or rural counties (7.2%), p < 0.0001. At a level of hospital referral region, PET utilization rates ranged from 2.2 to 20.8%. PET imaging was more commonly performed in White compared to Black or Hispanic patients. Rural patients were less likely to undergo PET imaging compared to metro patients (odds ratio [OR] 0.87, 95% Confidence interval [CI]: 0.82-0.92 p < 0.0001). Rural Black (OR 0.69, 95%CI 0.57-0.83, p < 0.0001) and rural White patients (OR 0.89, 95%CI 0.83-0.94 p < 0.0001) were less likely to obtain PET imaging compared to their metro counterparts, p-interaction < 0.0001.

Conclusion: Rural patients were less likely to undergo PET imaging than metro patients. The effect of rurality was most pronounced among Black patients. Our findings underscore the need for strategies to support equitable use of PET imaging.

Objective: Platinum resistance carries poor prognosis in epithelial ovarian carcinoma (EOC). This study aimed to assess the value of radiomics model based on contrast-enhanced CT (ceCT) in predicting response to platinum-based chemotherapy in EOC.

Materials and methods: Patients with histologically confirmed EOC and pre-treatment ceCT were retrospectively recruited from 5 centres. All patients underwent standard platinum-based chemotherapy and optimal cytoreduction. Platinum sensitivity was determined by whether it recurred within six months after platinum-based chemotherapy. The whole tumour volume was manually segmented on the baseline ceCT. Radiomics features were extracted using the open-source package PyRadiomics (version 3.0.1). Patients from centres A-C were randomly divided into training and internal validation sets in 4:1 ratio. Patients from the centres D and E were assigned as independent external validation sets. Spearman's rank correlation followed by 5-fold stratified cross validation (SCV) elastic net repeated for 100 times, and Mann-Whitney U test were deployed for feature reduction and selection. Adaptive synthetic sampling was applied to minimize class biases. Extra Trees classifier across 10-fold SCV was used for model building. The area under curve (AUC), calibration curve assessment, and decision curve analysis (DCA) were deployed to evaluate model performance and translational clinical utility.

Results: Seven hundred and three EOC patients (51.6 ± 9.3 years) were recruited. The training data (n = 608) yielded the following classification metrics: AUC (0.917), sensitivity (83.9%), specificity (94.4%), and accuracy (91.7%) in the internal validation set. The external validation set using centre D (n = 44) had AUC (0.877), sensitivity (76.5%), specificity (92.6%), and accuracy (86.4%); while centre E (n = 51) had AUC (0.845), sensitivity (73.3%), specificity (86.1%), and accuracy (82.4%) in predicting platinum sensitivity. DCA illustrated net clinical benefit in internal validation set and both external validation sets.

Conclusions: The proposed CT-based radiomics model could be useful in predicting platinum sensitivity in EOC with potential in guiding personalized treatment in EOC.

Background: The long noncoding RNA Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) has been extensively studied as an oncogenic factor. Antisense oligonucleotides (ASOs) labeled with the Cyanine5.5 (Cy5.5) dye enable effective in vivo imaging using near-infrared fluorescence.

Methods: Pan-cancer research on MALAT1 expression levels was conducted through The Cancer Genome Atlas (TCGA) database analysis. The selectivity and specificity of MALAT1-ASO were validated in lung cancer and epidermal carcinoma cell lines (A549, A431, PC9GR, and PC9) using cellular fluorescence and flow cytometry. Corresponding xenograft models were created for these cell lines, and near-infrared fluorescence imaging assessed tumor imaging effectiveness and the biodistribution of Cy5.5-labeled MALAT1 ASOs.

Results: MALAT1 expression levels were found to be upregulated in various tumors and high MALAT1 expression level correlated to poor prognosis in some tumors. The high expression of MALAT1 was confirmed in tumor cell lines. In vitro fluorescent intensity correlated with MALAT1 expression within cells. The fluorescence intensity also exhibited concentration dependence. In vivo experiments revealed a significant contrast between tumor tissues and normal tissues within 24 h. Tumors exhibited varied probe uptake corresponding to their MALAT1 expression levels. Ex vivo experiments shows high probe uptake in kidney, liver and intestine tissues.

Conclusion: MALAT1 is highly expressed in various cancer tissues and associated with poor prognosis. In xenograft models of lung cancer and epidermal carcinoma cell lines A549, A431, PC9GR, and PC9, Cy5.5-labeled ASOs exhibit evident binding specificity and discernible imaging effect in both in vitro and in vivo, effectively reflecting MALAT1 expression levels in tumors.

Background: The utility of threshold growth (TG) in hepatocellular carcinoma (HCC) imaging remains contentious across major guidelines. This study aimed to investigate the diagnostic implications of TG in HCC diagnosis using the criteria set by the Liver Imaging Reporting and Data System (LI-RADS).

Methods: In this single-center retrospective study, three radiologists independently evaluated pre-transplantation hepatobiliary agent-enhanced MR images and prior CT/MR images using LI-RADS v2018 in consecutive patients who underwent liver transplantation between January 2010 and November 2022. TG was defined as a ≥ 50% size increase in ≤ 6 months. Explanted livers served as reference standards. Frequencies of TG between HCCs and non-HCCs were compared using Fisher's exact test, and interobserver agreement was assessed using Fleiss κ statistics. The diagnostic performance of LI-RADS category 5 in the diagnosis of HCC was assessed with and without considering TG as a major feature. McNemar tests were used to compare results.

Results: The cohort included 158 patients (mean age, 59.1 ± 7.5 years; 130 males) with 280 observations (207 HCCs, 5 non-HCC malignancies, and 68 benign lesions). TG was identified in 44 (15.7%) observations. Interobserver agreement on TG was moderate (κ = 0.280). Incorporating TG as a major feature significantly enhanced the sensitivity of LI-RADS category 5 in diagnosing HCC (33.8% vs. 40.6%, p < 0.001) without compromising specificity (100.0% vs. 94.5%, p = 0.125).

Conclusions: Incorporating TG as a major criterion in LI-RADS category 5 enhanced the diagnostic sensitivity for HCC in liver transplant candidates with minimal impact on specificity. However, TG demonstrated a variable interobserver agreement.

Trial registration: Not applicable.

Objective: To develop and validate a multimodal scoring system integrating clinical, radiological, and pathological variables to preoperatively predict extraprostatic extension (EPE) in prostate cancer (PCa).

Methods: This retrospective study included 667 PCa patients divided into a derivation cohort and two validation cohorts. Evaluated parameters comprised prostate-specific antigen density (PSAD), curvilinear contact length (CCL), lesion longest diameter (LD), National Cancer Institute EPE grade (NCI_EPE), International Society of Urological Pathology grade (ISUP), and other relevant variables. Independent predictors were identified through univariate and multivariate regression analysis to construct a logistic model. Coefficients from this model were then weighted to establish a scoring system. The predictive performance of the NCI_EPE, logistic model, and scoring system was systematically evaluated and compared. Finally, the scoring system was stratified into four distinct risk categories.

Results: Multivariate analysis identified NCI_EPE, PSAD, CCL/LD, and ISUP as independent predictors of EPE. In the derivation and validation cohorts, the scoring system demonstrated robust predictive accuracy for EPE, with AUCs of 0.849, 0.830, and 0.847, respectively. These values outperformed the NCI_EPE (Derivation cohort: 0.849 vs. 0.750, P < 0.003, Validation cohort 1: 0.830 vs. 0.736, P = 0.138, Validation cohort 2: 0.837 vs. 0.715, P = 0.003) and were comparable to the logistic model (Derivation cohort: 0.849 vs. 0.860, P = 0.228, Validation cohort 1: 0.830 vs. 0.849, P = 0.711, Validation cohort 2: 0.837 vs. 0.843, P = 0.738). Decision curve analysis revealed higher net clinical benefit for both the scoring system and logistic model compared to the NCI_EPE. Risk stratification using the scoring system categorized patients into four tiers: low (0-3), intermediate-low (4-6), intermediate-high (7-9), and high risk (10-12) with corresponding mean EPE probabilities of 9.9%, 26.0%, 52.0%, and 85.0%. These probabilities closely aligned with observed pT3 incidences in the derivation and validation cohorts.

Conclusions: The scoring system provides enhanced predictive accuracy for EPE, preoperatively stratifying patients into distinct risk categories to facilitate personalized therapeutic strategies.

Background: Neuroendocrine tumors have increased in prevalence and diversity in recent years and are often diagnosed at metastatic stages. Compared with nonradioactive systemic treatment with somatostatin analogs, peptide receptor radionuclide therapy (PRRT) has shown superior overall survival benefits for well-differentiated neuroendocrine tumor patients. This study aimed to identify biomarkers from ⁶⁸Ga‒DOTATOC PET/CT scans to predict survival in patients treated with PRRT in the clinic.

Methodology: This retrospective study analyzed ⁶⁸Ga-DOTATOC PET/CT data from 67 NET patients undergoing PRRT. Tumor volumes and SUV metrics were segmented using standardized protocols. Radiomics features from liver metastases were extracted and preprocessed for analysis. Data were analysed via Kaplan-Meier, Cox regression, and PCA to evaluate the prognostic value of volumetric-, radiomics-, and clinicopathological parameters.

Results: This study included scans from 67 patients with an average age of 67 years. The mean survival time was 46.5 months, with 43% of patients alive or lost to follow-up at the conclusion of data collection. Despite comprehensive analyses, neither volumetric parameters, including total tumor volume and organ-specific tumor volume, nor SUV values (SUVmax and SUVmean) were robust predictors of overall survival. K‒M and Cox regression analyses revealed no significant differences in survival between the high- and low-risk groups for these parameters. Furthermore, radiomics features extracted from liver metastases did not demonstrate significant prognostic value.

Conclusion: Quantification of ⁶⁸Ga-DOTATOC PET/CT-derived parameters offers limited prognostic value for OS in NET patients who are receiving PRRT in clinical practice. These findings might emphasize the current robust integration of imaging in clinical decision-making for NET management.

Background: In classical Hodgkin lymphoma (HL), optimizing early risk stratification and response assessment are the cornerstones of therapy. The advanced interpretation of positron emission tomography - computed tomography (PET/CT) results can provide prognostic information beyond the Deauville score (DS). The aim of our study was to explore the prognostic value of the change in maximum standardized uptake value (ΔSUVmax) to predict disease progression during the first-line treatment of adult HL.

Methods: All patients were treated with curative intent, standard therapy. PET/CT assessments were performed at baseline, interim and end-of-treatment timepoints. ΔSUVmax cut-off values were determined by the receiver operating characteristics (ROC) analysis. Overall- (OS) and progression-free survival (PFS) were determined as primary endpoints.

Results: Baseline SUVmax did not differ in patients who progressed during or after first-line therapy compared to patients in remission. However, patients with progressive disease had a higher mean SUVmax and lower ΔSUVmax at interim analysis. The presence of a ΔSUVmax > 88% after 2 cycles of therapy was associated with longer PFS (P = 0.013 [HR, 5.21]), with a negative predictive value exceeding the DS. The combination of ΔSUVmax with DS further stratified PET-negative patients: the 5-year PFS of low-risk and high-risk patients were 92.1% and 79.1%, respectively (P = 0.047 [HR, 2.87]). The ΔSUVmax cut-off of 55% in patients with DS 3-5 revealed high-risk patients with significantly lower 5-year OS and PFS (P = 0.008 [HR, 13] and P < 0.001 [HR, 11.5], respectively).

Conclusions: Altogether, ΔSUVmax is a promising standalone prognostic marker or combination partner of DS in the early risk stratification and response assessment of HL.

Background: Intraoperative bleeding is a serious complication of spinal tumor surgery. Preoperative identification of patients at high risk of intraoperative blood transfusion (IBT) and intraoperative massive bleeding (IMB) before spinal tumor resection surgery is difficult but critical for surgical planning and blood management. This study aims to develop and validate delta radiomics prediction models for IBT and IMB in spinal tumor surgery.

Methods: Patients diagnosed with spinal tumors who underwent spinal tumor resection surgery were retrospectively recruited. CT, CTE, delta, and clinical models based on CT native phase, CT arterial phase images, and clinical factors were constructed using 10-fold cross-validation and logistic regression (LR), random forest (RF), and support vector machine (SVM) in the training cohort. Receiver operating characteristic (ROC) curves, integrated discrimination improvement (IDI), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate and compare the diagnostic performance of these models.

Results: 231 patients were randomly divided into training (n = 161) and test (n = 70) cohorts, comprising 146 IBT and 85 no-IBT patients, 35 IMB and 196 no-IMB patients, respectively. The delta model performed best in predicting IBT and IMB risk, with better predictive ability than the clinical model (IDI = 0.11-0.13 for IBT, and IDI = 0.02-0.08 for IMB, p < 0.05, respectively). Calibration curves indicated that the predicted probabilities of IBT and IMB in the model did not differ significantly from the actual probabilities (p > 0.05).

Conclusion: The CT delta model we constructed may be a valuable tool to improve risk stratification before spinal tumor surgery, thus contributing to preoperative planning and improving patient prognosis.

Trial registration: Retrospectively registered (M2020435).