Cancer Imaging最新文献

Background: Intratumor heterogeneity (ITH) is a key biological characteristic of gliomas. This study aimed to characterize ITH in adult-type diffuse gliomas and assess the feasibility of using habitat imaging based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) to preoperatively predict isocitrate dehydrogenase (IDH) genotypes and prognosis.

Methods: Sixty-three adult-type diffuse gliomas with known IDH genotypes were enrolled. Volume transfer constant (K^trans) and apparent diffusion coefficient (ADC) maps were acquired from DCE-MRI and DWI, respectively. After tumor segmentation, the k-means algorithm clustered K^trans and ADC image voxels to generate spatial habitats and extract quantitative image features. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to evaluate IDH predictive performance. Multivariable logistic regression models were constructed and validated using leave-one-out cross-validation, and the contrast-enhanced subgroup was analyzed independently. Kaplan-Meier and Cox proportional hazards regression analyses were used to investigate the relationship between tumor habitats and progression-free survival (PFS) in the two IDH groups.

Results: Three habitats were identified: Habitat 1 (hypo-vasopermeability and hyper-cellularity), Habitat 2 (hypo-vasopermeability and hypo-cellularity), and Habitat 3 (hyper-vasopermeability). Compared to the IDH wild-type group, the IDH mutant group exhibited lower mean K^trans values in Habitats 1 and 2 (both P < 0.001), higher volume (P < 0.05) and volume percentage (pVol, P < 0.01) of Habitat 2, and lower volume and pVol of Habitat 3 (both P < 0.001). The optimal logistic regression model for IDH prediction yielded an AUC of 0.940 (95% confidence interval [CI]: 0.880-1.000), which improved to 0.948 (95% CI: 0.890-1.000) after cross-validation. Habitat 2 contributed the most to the model, consistent with the findings in the contrast-enhanced subgroup. In IDH wild-type group, pVol of Habitat 2 was identified as a significant risk factor for PFS (high- vs. low-pVol subgroup, hazard ratio = 2.204, 95% CI: 1.061-4.580, P = 0.034), with a value below 0.26 indicating a 5-month median survival benefit.

Conclusions: Habitat imaging employing DCE-MRI and DWI may facilitate the characterization of ITH in adult-type diffuse gliomas and serve as a valuable adjunct in the preoperative prediction of IDH genotypes and prognosis.

Clinical trial number: Not applicable.

Background: The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.

Methods: A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.

Results: A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.

Conclusion: An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.

Background: Previous studies utilizing dual-energy CT (DECT) for evaluating treatment efficacy in nasopharyngeal cancinoma (NPC) are limited. This study aimed to investigate whether the parameters from DECT can predict the response to induction chemotherapy in NPC patients in two centers.

Methods: This two-center retrospective study included patients diagnosed with NPC who underwent contrast-enhanced DECT between March 2019 and November 2023. The clinical and DECT-derived parameters of tumor lesions were calculated to predict the response. We employed univariate and multivariate analysis to identify significant factors. Subsequently, the clinical, DECT, and clinical-DECT nomogram models were developed using independent predictors in the training cohort and validated in the test cohort. Receiver operating characteristic analysis was performed to evaluate the models' performance.

Results: A total of 321 patients were included in the study, predominantly male [247 (76.9%)] with an average age of 52.04 ± 10.87 years. The training cohort (Center 1) comprised 252 patients, while the test cohort (Center 2) comprised 69 patients. Of these, 233 out of 321 patients (72.6%) were responders to induction chemotherapy. The clinical-DECT nomogram showed an AUC of 0.805 (95% CI, 0.688-0.906), outperforming both the DECT model (Extracellular volume fraction [ECVf]) (AUC, 0.706 [95% CI, 0.571-0.825]) and the clinical model (Ki67) (AUC, 0.693 [95% CI, 0.580-0.806]) in the test cohort.

Conclusions: Ki67 and ECVf emerged as independent predictive factors for response to induction chemotherapy in NPC patients. The proposed nomogram, incorporating ECVf, demonstrated accurate prediction of treatment response.

True total-body and extended axial field-of-view (AFOV) PET/CT with 1m or more of body coverage are now commercially available and dramatically increase system sensitivity over conventional AFOV PET/CT. The Siemens Biograph Vision Quadra (Quadra), with an AFOV of 106cm, potentially allows use of significantly lower administered radiopharmaceuticals as well as reduced scan times. The aim of this study was to optimise acquisition protocols for routine clinical imaging with FDG on the Quadra the prioritisation of reduced activity given physical infrastructure constraints in our facility. Low-dose (1 MBq/kg) and ultra-low dose (0.5 MBq/g) cohorts, each of 20 patients were scanned in a single bed position for 10 and 15 min respectively with list-mode data acquisition. These data were then reconstructed simulating progressively shorter acquisition times down to 30 s and 1 min, respectively and then reviewed by 2 experienced PET readers who selected the shortest optimal and minimal acquisition durations based on personal preferences. Quantitative analysis was also performed of image noise to assess how this correlated with qualitative preferences. At the consensus minimum acquisition durations at both dosing levels, the coefficient of variance in the liver as a measure of image noise was 10% or less and there was minimal reduction in this measure between the optimal and longest acquisition durations. These data support the reduction in both administered activity and scan acquisition times for routine clinical FDG PET/CT on the Quadra providing efficient workflows and low radiation doses to staff and patients, while achieving high quality images.

Background: Programmed cell death 1/programmed death ligand-1 (PD-L1)-based immune checkpoint blockade is an effective treatment approach for non-small-cell lung cancer (NSCLC). However, immunohistochemistry does not accurately or dynamically reflect PD-L1 expression owing to its spatiotemporal heterogeneity. Herein, we assessed the feasibility of using a ⁶⁸Ga-labeled anti-PD-L1 nanobody, ⁶⁸Ga-NODAGA-NM-01, for PET imaging of PD-L1.

Methods: Micro-PET/CT and biodistribution studies were performed on PD-L1-positive and -negative tumor-bearing mice. Additionally, a preliminary clinical study was performed on two patients with NSCLC. NM-01 was radiolabeled with ⁶⁸Ga without further purification under mild conditions.

Results: ⁶⁸Ga-NODAGA-NM-01 exhibited radiochemical purity (> 98%), high stability in vitro, and rapid blood clearance in vivo. Specific accumulation of ⁶⁸Ga-NODAGA-NM-01 was observed in PD-L1-positive tumor-bearing mice, with a good tumor-to-background ratio 0.5h post-injection. Furthermore, ⁶⁸Ga-NODAGA-NM-01 PET/CT imaging was found to be safe with no adverse events and distinct uptake in primary and metastatic lesions of the PD-L1-positive patient, with a higher maximal standardized uptake value than that in lesions of the PD-L1-negative patient 1h post-injection.

Conclusions: ⁶⁸Ga-NODAGA-NM-01 can be prepared using a simple method under mild conditions and reflect PD-L1 expression in primary and metastatic lesions. However, our findings need to be confirmed in a large cohort.

Trial registration: NCT02978196. Registered February 15, 2018.

Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.

Materials and methods: We retrospectively retrieved 154 cases of recurrent HGG from multiple centers. Tumor segmentation was performed by expert radiologists and a convolutional neural network (CNN). From the segmented tumors, 2553 radiomic features were extracted for each case. A robust feature subset was selected using intraclass correlation coefficient analysis between manual and automated segmentations. The data was divided into a 9:1 ratio and validated through ten-fold cross-validation and tested on a rotating test set. Features selection was done by the Kruskal-Wallis test. The Radiomics-based OS predictions, generated using Support Vector Machine (SVM), were compared between the two segmentation approaches and against OS prediction by the CNN model adapted for classification. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC).

Results: The clinical model AUC for OS prediction was 0.640 ± 0.013 (mean ± 95% confidence interval) in the training set and 0.610 ± 0.131 in the test set. The radiomics prediction of OS based on manual segmentation outperformed automatic segmentation (AUC of 0.662 ± 0.122 vs. 0.471 ± 0.086, respectively) in the test set. Robust features improved the performance of manual segmentation to AUC of 0.700 ± 0.102, of automated segmentation to 0.554 ± 0.085. The CNN prognosis model demonstrated promising results, with an average AUC of 0.755 ± 0.071 for training sets and 0.700 ± 0.101 for the test set.

Conclusion: Manual segmentation-derived radiomic features outperformed automated segmentation-derived features for predicting OS in recurrent high-grade glioma patients undergoing immunotherapy. The end-to-end CNN prognosis model performed similarly to radiomics modeling using manual-segmentation-derived features without the need for segmentation. The potential time-saving must be weighed against the lower interpretability of end-to-end black box modeling.

Objectives: To establish and validate a dual-modal radiomics nomogram from grayscale ultrasound and color doppler flow imaging (CDFI) of cervical lymph nodes (LNs), aiming to improve the diagnostic accuracy of metastatic LNs in differentiated thyroid carcinoma (DTC).

Methods: DTC patients with suspected cervical LNs in two medical centers were retrospectively enrolled. Pathological results were set as gold standard. We extracted radiomic characteristics from grayscale ultrasound and CDFI images, then applied lasso (least absolute shrinkage and selection operator) regression analysis to analyze radiomics features and calculate the rad-score. A nomogram based on rad-score, clinical data, and ultrasound signs was developed. The performance of the model was evaluated using AUC and calibration curve. We also assessed the model's diagnostic ability in European Thyroid Association (ETA) indeterminate LNs.

Results: 377 DTC patients and 726 LNs were enrolled. 37 radiomics features were determined and calculated as rad-score. The dual-modal radiomics model showed good calibration capabilities. The radiomics model displayed higher diagnostic ability than the traditional ultrasound model in the training set [0.871 (95% CI: 0.839-0.904) vs. 0.848 (95% CI: 0.812-0.884), p<0.01], internal test set [0.804 (95% CI: 0.741-0.867) vs. 0.803 (95% CI: 0.74-0.866), p = 0.696], and external validation cohort [0.939 (95% CI: 0.893-0.984) vs. 0.921 (95% CI: 0.857-0.985), p = 0.026]. The radiomics model could also significantly improve the detection rate of metastatic LNs in the ETA indeterminate LN category.

Conclusions: The dual-modal radiomics nomogram can improve the diagnostic accuracy of metastatic LNs of DTC, especially for LNs in ETA indeterminate classification.

Background: Radiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enhance the stratification and analysis of therapeutic responses.

Materials and methods: This multicenter study incorporated various data types from 506 NSCLC patients, which underwent preprocessing through anonymization methods and were securely encrypted using the AES-CBC algorithm. We developed one clinical model and three radiomics models based on clinical factors and radiomics scores (RadScore) of three distinct regions to evaluate treatment response. Additionally, an integrated radiomics-clinical model was created by combining clinical factors with RadScore. The study also explored the association between different EGFR mutations and PD-1/PD-L1 expression in radiomics biomarkers.

Findings: The radiomics-clinical model demonstrated high performance, with AUC values as follows: EGFR (0.884), 19Del (0.894), L858R (0.881), T790M (0.900), and PD-1/PD-L1 expression (0.893) in the test set. This model outperformed both clinical and single radiomics models. Decision curve analysis further supported its superior clinical utility. Additionally, our findings suggest that the efficacy of EGFR-TKIs and ICIs therapy may not depend on detecting a singular tumor feature or cell type.

Conclusion: The proposed method effectively balances the level of evidence with privacy protection, enhancing the study's validity and security. Therefore, radiomics biomarkers are expected to complement molecular biology analyses and guide therapeutic strategies for EGFR-TKIs, ICIs, and their combinations.

Background: Current diagnostic imaging modalities have limited ability to differentiate between malignant and benign pancreaticobiliary disease, and lack accuracy in detecting lymph node metastases. ¹⁸F-Prostate-Specific Membrane Antigen (PSMA) PET/CT is an imaging modality used for staging of prostate cancer, but has incidentally also identified PSMA-avid pancreatic lesions, histologically characterized as pancreatic ductal adenocarcinoma (PDAC). This phase I/II study aimed to assess the feasibility of ¹⁸F-PSMA PET/CT to detect PDAC.

Methods: Seventeen patients with clinically resectable PDAC underwent ¹⁸F-PSMA PET/CT prior to surgical resection. Images were analyzed both visually and (semi)quantitatively by deriving the maximum standardized uptake value (SUV_max) and tumor-to-background ratio (TBR). TBR was defined as the ratio between SUV_max of the primary tumor divided by SUV_max of the aortic blood pool. Finally, tracer uptake on PET was correlated to tissue expression of PSMA in surgical specimens.

Results: Out of 17 PSMA PET/CT scans, 13 scans demonstrated positive PSMA tracer uptake, with a mean SUV_max of 5.0 ± 1.3. The suspected primary tumor was detectable (TBR ≥ 2) with a mean TBR of 3.3 ± 1.3. For histologically confirmed PDAC, mean SUV_max and mean TBR were 4.9 ± 1.2 and 3.3 ± 1.5, respectively. Although eight patients had histologically confirmed regional lymph node metastases and two patients had distant metastases, none of these metastases demonstrated ¹⁸F-PSMA uptake. There was no correlation between ¹⁸F-PSMA PET/CT SUV_max and tissue expression of PSMA in surgical specimens.

Conclusions: ¹⁸F-PSMA PET/CT was able to detect several pancreaticobiliary cancers, including PDAC. However, uptake was generally low, not specific to PDAC and no tracer uptake was observed in lymph node or distant metastases. The added value of PSMA PET in this setting appears to be limited.

Trial registration: The trial is registered as PANSCAN-2 in the European Clinical Trials Database (EudraCT number: 2020-002185-14).