The impact of deep-inspiration breath-hold total-body PET/CT imaging on thoracic 18F-FDG avid lesions compared with free-breathing.

OBJECTIVES: To investigate PET/CT registration and quantification accuracy of thoracic lesions of a single 30-second deep-inspiration breath-hold (DIBH) technique with a total-body PET (TB-PET) scanner, and compared with free-breathing (FB) PET/CT. METHODS: 137 of the 145 prospectively enrolled patients finished a routine FB-300 s PET/CT exam and a 30-second DIBH TB-PET with chest to pelvis low dose CT. The total-body FB-300 s, FB-30 s, and DIBH-30 s PET images were reconstructed. Quantitative assessment (SUVmax and SUVmean of lung and other organs), PET/CT registration assessment and lesion analysis (SUVmax, SUVpeak, SUVmean and tumor-background ratio) were compared with Wilcoxon signed-rank tests. RESULTS: The SUVmax and SUVmean of the lung with DIBH-30 s were significantly lower than those with FB. The distances of the liver dome between PET and CT were significantly smaller with DIBH-30 s than with FB. 195 assessable lesions in 106 patients were included, and the detection sensitivity was 97.9 % and 99.0 % in FB-300 s, and DIBH-30 s, respectively. For both small co-identified lesions (n = 86) and larger co-identified lesions with a diameter ≥ 1 cm (n = 91), the lesion SUVs were significantly greater with DIBH-30 s than with FB-300 s. Regarding lesion location, the differences of the SUVs for the lesions in the lower thorax area (n = 97, p < 0.001) were significant between DIBH-30 s and FB-300 s, while these differences were not statistically significant in the upper thorax (n = 80, p > 0.05). The lesion tumor-to-surrounding-background ratio (TsBR) was significantly increased, both in the upper and lower thorax. CONCLUSION: The TB DIBH PET/CT technique is feasible in clinical practice. It reduces the background lung uptake and achieves better registration and lesion quantification, especially in the lower thorax.

Advantages and Challenges of Total-Body PET/CT at a Tertiary Cancer Center: Insights from Sun Yat-sen University Cancer Center.

In recent decades, researchers worldwide have directed their efforts toward enhancing the quality of PET imaging. The detection sensitivity and image resolution of conventional PET scanners with a short axial field of view have been constrained, leading to a suboptimal signal-to-noise ratio. The advent of long-axial-field-of-view PET scanners, exemplified by the uEXPLORER system, marked a significant advancement. Total-body PET imaging possesses an extensive scan range of 194 cm and an ultrahigh detection sensitivity, and it has emerged as a promising avenue for improving image quality while reducing the administered radioactivity dose and shortening acquisition times. In this review, we elucidate the application of the uEXPLORER system at the Sun Yat-sen University Cancer Center, including the disease distribution, patient selection workflow, scanning protocol, and several enhanced clinical applications, along with encountered challenges. We anticipate that this review will provide insights into routine clinical practice and ultimately improve patient care.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

BACKGROUND: Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE: To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE: Retrospective. POPULATION: One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE: DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT: The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS: The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS: The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION: The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs. PURPOSE: To develop and assess the performance of a DL-based approach for diagnosing and grading PFOA on axial radiographs. MATERIAL AND METHODS: A total of 1280 (dataset 1) axial radiographs were retrospectively collected and utilized to develop the high-resolution network (HRNet)-based classification models. The ground truth was the interpretation from two experienced radiologists in consensus according to the K-L grading system. A binary-class model was trained to diagnose the presence (K-L 2∼4) or absence (K-L 0∼1) of PFOA. A multi-class model was used to grade the stage of PFOA, i.e. from K-L 0 to K-L 4. Model performances were evaluated using the receiver operating characteristics (ROC), confusion matrix, and the corresponding evaluation metrics (positive predictive value [PPV], negative predictive value [NPV], F1 score, sensitivity, specificity, accuracy) of the internal test set (n = 129) from dataset 1 and an external validation set (dataset 2, n = 187). RESULTS: For the binary-class model, the area under the curve (AUC) was 0.91 in the internal test set and 0.90 in the external validation set. For grading PFOA, moderate to severe stage of PFOA exhibited a good performance in these two datasets (AUC = 0.91-0.98, PPV = 0.69-0.90, NPV = 0.92-0.99, F1 score = 0.72-0.87, sensitivity = 0.75-0.87, specificity = 0.90-0.99, accuracy = 0.87-0.98). CONCLUSION: The HRNet-based approach performed well in diagnosing and grading radiographic PFOA, especially for the moderate to severe cases.

18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy.

RATIONALE AND OBJECTIVES: To develop a combined model incorporating the clinical and PET features for identifying patients with diffuse large B-cell lymphoma (DLBCL) at high risk of progression or relapse after first-line therapy, compared to International Prognostic Index (IPI) and Deauville score (DS) assessment. MATERIALS AND METHODS: 271 18F-FDG PET images with DLBCL were retrospectively collected and randomly divided into the training (n=190) and test dataset (n=81). All visible lesions were annotated. Baseline, end-of-treatment (EoT), and delta PET radiomics features were extracted. IPI model, the baseline clinical model group (MG), DS model, the combined clinical MG, the PET-based radiomics MG, and the combined MG were constructed to predict 2-year time to progression (2Y-TTP). For each MG, the cross-combination method was performed to generate 1680 candidate models based on three normalization methods, 20 features, 4 feature-selection methods, and 7 classifiers. The model achieving the highest AUC was selected as the best for each MG. Cox regression analysis was further performed. RESULTS: In the test set, the best combined model showed better discriminative power compared to IPI model, the best baseline clinical model, DS model, the best combined clinical model, and the best PET-based radiomics model (AUC 0.898 vs. 0.584, 0.695, 0.756, 0.824, 0.832; p < 0.001, 0.014, 0.018, 0.152, 0.042, respectively). The combined model was superior to other models for progression-free-survival prediction (C-index: 0.853 vs. 0.568, 0.666, 0.753, 0.808, 0.814, respectively). CONCLUSION: A combined model for identifying patients at high risk of progression or relapse after first-line therapy was constructed, superior to IPI and DS assessment.

Comprehensive 18F-FDG PET-based radiomics in elevating the pathological response to neoadjuvant immunochemotherapy for resectable stage III non-small-cell lung cancer: A pilot study.

Purpose: To develop a comprehensive PET radiomics model to predict the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III non-small-cell lung cancer (NSCLC) patients. Methods: Stage III NSCLC patients who received three cycles of neoadjuvant toripalimab with chemotherapy and underwent 18F-FDG PET/CT were enrolled. Baseline 18F-FDG PET/CT was performed before treatment, and preoperative 18F-FDG PET/CT was performed three weeks after the completion of neoadjuvant treatment. Surgical resection was performed 4-5 weeks after the completion of neoadjuvant treatment. Standardized uptake value (SUV) statistics features and radiomics features were derived from baseline and preoperative PET images. Delta features were derived. The radiologic response and metabolic response were assessed by iRECIST and iPERCIST, respectively. The correlations between PD-L1 expression, driver-gene status, peripheral blood biomarkers, and the pathological responses (complete pathological response [CPR]; major pathological response [MPR]) were assessed. Associations between PET features and pathological responses were evaluated by logistic regression. Results: Thirty patients underwent surgery and 29 of them performed preoperative PET/CT. Twenty patients achieved MPR and 16 of them achieved CPR. In univariate analysis, five SUV statistics features and two radiomics features were significantly associated with pathological responses. In multi-variate analysis, SUVmax, SUVpeak, SULpeak, and End-PET-GLDM-LargeDependenceHighGrayLevelEmphasis (End-GLDM-LDHGLE) were independently associated with CPR. SUVpeak and SULpeak performed better than SUVmax and SULmax for MPR prediction. No significant correlation, neither between the radiologic response and the pathological response, nor among PD-L1, driver gene status, and baseline PET features was found. Inflammatory response biomarkers by peripheral blood showed no difference in different treatment responses. Conclusion: The logistic regression model using comprehensive PET features contributed to predicting the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III NSCLC patients.

Preoperative Prediction of Inferior Vena Cava Wall Invasion of Tumor Thrombus in Renal Cell Carcinoma: Radiomics Models Based on Magnetic Resonance Imaging.

Objective: To develop radiomics models to predict inferior vena cava (IVC) wall invasion by tumor thrombus (TT) in patients with renal cell carcinoma (RCC). Methods: Preoperative MR images were retrospectively collected from 91 patients with RCC who underwent radical nephrectomy (RN) and thrombectomy. The images were randomly allocated into a training (n = 64) and validation (n = 27) cohort. The inter-and intra-rater agreements were organized to compare masks delineated by two radiologists. The masks of TT and IVC were manually annotated on axial fat-suppression T2-weighted images (fsT2WI) by one radiologist. The following models were trained to predict the probability of IVC wall invasion: two radiomics models using radiomics features extracted from the two masks (model 1, radiomics model_IVC; model 2, radiomics model_TT), two combined models using radiomics features and radiological features (model 3, combined model_IVC; model 4, combined model_TT), and one radiological model (model 5) using radiological features. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were applied to validate the discriminatory effect and clinical benefit of the models. Results: Model 1 to model 5 yielded area under the curves (AUCs) of 0.881, 0.857, 0.883, 0.889, and 0.769, respectively, in the validation cohort. No significant differences were found between these models (p = 0.108-0.951). The dicision curve analysis (DCA) showed that the model 3 had a higher overall net benefit than the model 1, model 2, model 4, and model 5. Conclusions: The combined model_IVC (model 3) based on axial fsT2WI exhibited excellent predictive performance in predicting IVC wall invasion status.

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.

OBJECTIVE: To establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images. METHODS: The model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level. RESULTS: The pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were <15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images. CONCLUSION: The deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.

Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.

BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. METHODS: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen's kappa coefficient. RESULTS: In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892-0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. CONCLUSION: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network.

BACKGROUND: Accurate segmentation of pelvic bones is an initial step to achieve accurate detection and localisation of pelvic bone metastases. This study presents a deep learning-based approach for automated segmentation of normal pelvic bony structures in multiparametric magnetic resonance imaging (mpMRI) using a 3D convolutional neural network (CNN). METHODS: This retrospective study included 264 pelvic mpMRI data obtained between 2018 and 2019. The manual annotations of pelvic bony structures (which included lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis) on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images were used to create reference standards. A 3D U-Net CNN was employed for automatic pelvic bone segmentation. Additionally, 60 mpMRI data from 2020 were included and used to evaluate the model externally. RESULTS: The CNN achieved a high Dice similarity coefficient (DSC) average in both testing (0.80 [DWI images] and 0.85 [ADC images]) and external (0.79 [DWI images] and 0.84 [ADC images]) validation sets. Pelvic bone volumes measured with manual and CNN-predicted segmentations were highly correlated (R2 value of 0.84-0.97) and in close agreement (mean bias of 2.6-4.5 cm3). A SCORE system was designed to qualitatively evaluate the model for which both testing and external validation sets achieved high scores in terms of both qualitative evaluation and concordance between two readers (ICC = 0.904; 95% confidence interval: 0.871-0.929). CONCLUSIONS: A deep learning-based method can achieve automated pelvic bone segmentation on DWI and ADC images with suitable quantitative and qualitative performance.

Quantitative evaluation of chronically obstructed kidneys from noncontrast computed tomography based on deep learning.

OBJECTIVE: To quantitatively report renal parenchymal volume (RPV), renal sinus volume (RSV), and renal parenchymal density (RPD) for chronically obstructed kidneys from noncontrast computed tomography (NCCT). METHODS: This retrospective study was approved by the institutional review board of our hospital with a waiver of informed consent. We retrospectively collected 304 consecutive NCCT scans of urinary obstruction and constructed two datasets: one with 167 patient scans for parenchyma and sinus segmentation (segmentation dataset) and the other containing 137 scans from different patients diagnosed with chronic urinary obstruction (CUO dataset) and paired with split glomerular filtration rate (sGFR). A cascaded three-dimensional (3D) U-Net model was developed and validated for parenchyma and sinus segmentation. The RPV, RSV, and RPD of the CUO dataset were calculated by the model with manual editing. A multivariate analysis was performed to show the association between all parameters and the sGFR. RESULTS: In the test dataset, the Dice values for parenchyma and sinus segmentation were 0.95 ± 0.04 and 0.90 ± 0.05, respectively. Compared with those of nonobstructed kidneys, the RSV and RPD of obstructed kidneys increased, but RPV and sGFR decreased (P < .001). For chronically obstructed kidneys, age (r = -0.292, P < .001), RPV (r = 0.849, P < .001), RSV (r = -0.331, P < .001), and RPD (r = -0.296, P < .001) were significantly correlated with sGFR. The fitted regression model was sGFR = 10.873-0.111 Age + 0.211 RPV - 0.022 RSV (r2 = 0.712). CONCLUSIONS: NCCT combined with deep learning has the potential to be a single radiological procedure for morphological and functional evaluation of chronically obstructed kidneys.

Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.

OBJECTIVES: To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU). METHODS: Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve. RESULTS: The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst. CONCLUSIONS: The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst. KEY POINTS: • The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.

Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithometry: Combined Deep Learning and Thresholding Methods.

PURPOSE: To develop and validate a deep learning and thresholding-based model for automatic kidney stone detection and scoring according to S.T.O.N.E. nephrolithometry. PROCEDURES: Abdominal noncontrast computed tomography (NCCT) images were retrospectively archived from February 2018 to April 2019 for three parts: a segmentation dataset (n = 167), a hydronephrosis classification dataset (n = 282), and test dataset (n = 117). The model consisted of four steps. First, the 3D U-Nets for kidney and renal sinus segmentation were developed. Second, the deep 3D dual-path networks for hydronephrosis grading were developed. Third, the thresholding methods were used to detect and segment stones in the renal sinus region. The stone size, CT attenuation, and tract length were calculated from the segmented stone region. Fourth, the stone's location was determined. The stone detection performance was estimated with sensitivity and positive predictive value (PPV). The hydronephrosis grading and stone size, tract length, number of involved calyces, and essence grading were estimated with the area under the curve (AUC) method and linear-weighted κ statistics, respectively. RESULTS: The stone detection algorithm reached a sensitivity of 95.9 % (236/246) and a PPV of 98.7 % (236/239). The hydronephrosis classification algorithm achieved an AUC of 0.97. The scoring model results showed good agreement with radiologist results for the stone size, tract length, number of involved calyces, and essence grading (κ = 0.95, 95 % confidence interval [CI]: 0.92, 0.98; κ = 0.97, 95 % CI: 0.95, 1.00; κ = 0.95, 95 % CI: 0.92, 0.98; and κ = 0.97, 95 % CI: 0.94, 1.00), respectively. CONCLUSIONS: The scoring model was constructed that can automatically detect and score stones in NCCT images.

MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer.

BACKGROUND: Radiomics approaches based on multiparametric MRI (mp-MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). PURPOSE: To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. STUDY TYPE: Retrospective. POPULATION: In total, 210 patients with pathology-confirmed ECE status (101 positive, 109 negative) were enrolled. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and dynamic contrast-enhanced imaging were performed on two 3.0T MR scanners. ASSESSMENT: A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2 WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. STATISTICAL TESTS: A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. RESULTS: The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600-0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%-50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%-94.29%); P ranged from 0.64-1.00). DATA CONCLUSION: A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914-1925.

[Value of 18F-FDG PET/CT Scan Quantization Parameters for Prognostic Evaluation of Patients with Diffuse Large B-cells Lymphoma].

OBJECTIVE: To investigate the prognostic value of 18F-FDG PET/CT scan quantization parameters, max standardized uptake value （SUVmax）, metabolic tumor volume （MTV）, total lesion glycolysis （TLG） and other clinical factors for prognostic evaluation of paticnts with diffuse large B-cell lymphoma （DLBCL）. METHODS: PET/CT scan and clinical data of a total of 65 newly diagnosed DLBCL patients who received Rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone （R-CHOP） chemotherapy as first-line treatment were analyzed. All patients received a PET/CT scan at diagnosis and an interim PET/CT after 2-4 circles of chemotherapies. The related parameters of SUVmax, MTV and TLG were acquired by analyzing and calculating the scan results. The receiver operating characteristic （ROC） curve was used to determine the optimal cut-off of parameters. Pearson chi-square test, Kaplan-Meier method and COX proportional hazard model were performed to analyze the prognostic value of PET/CT related parameters and clinical factors in progression-free survival （PFS）. RESULTS: Age, B symptom, Ann Arbor stage and extra-nodal involvement in major organs significantly related with PFS （P<0.05）, but the SUVmax didn't relalt with the prognosis. The cut-off values of MTV0, MTV1, TLG0 and TLG1 for disease recurrence or progression were 172.20cm 3, 4.32cm 3, 1043.33g and 14.07g. The lower MTV and TLG groups showed longer PFS significantly. In the multivariate Cox regression model, B symptoms, MTV1 and TLG1 were the independent prognostic risk factors. CONCLUSION: MTV and TLG at baseline and in the interim and NCCN-IPI correlate with disease prognosis. SUVmax related parameters hare no significant relationship with prognosis. Besides MTV and TLG during treatment are the independent prognostic risk factors suggesting more predictive value than NCCN-IPI.