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Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN.A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics.Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit.The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.

To construct an FDG PET/CT metabolic parameter-based model to predict early recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT). A total of 62 patients with HCC after LT were enrolled with a follow-up period of 1 year. Basic clinical, pathology, and laboratory data, CT features (CPLC), and PET metabolic parameters (CPLCP) were collected for model construction. A CPLC nomogram without metabolic parameters and a CPLCP nomogram with metabolic parameters were established. The net reclassification index (NRI) and integrated discrimination improvement (IDI) of the two models were calculated. The constructed model was compared with Milan criteria and University of California San Francisco (UCSF) criteria. The time-dependent area under the receiver operating characteristic curve (time-AUC) was used to compare the efficiency of the models, and the bootstrap method was used to for verification. Harrell’s concordance index (C-index) was used to evaluate the performance of these models. Decision curve analysis (DCA) was used to evaluate the clinical practicability of each model. Thirty out of 62 patients experienced a recurrence during the 1-year follow-up. BCLC stage (P = 0.009), MVI (P = 0.032), AFP (P = 0.004), CTdmax (P = 0.033), and MTV (P = 0.039) were the independent predictors. The CPLC nomogram and the CPLCP nomogram were established. Compared with the CPLC nomogram, the NRI of the CPLCP nomogram increased by 38.98% (95% CI = −18.77–60.43%) and the IDI increased by 4.40% (95% CI = −1.00–16.62%). The AUC value of the CPLCP nomogram was higher than those of Milan criteria and UCSF criteria in the time-AUC curve. Moreover, the CPLCP nomogram had a higher C-index (0.774) than other models. Finally, the DCA curve showed that clinical practicability of the CPLCP nomogram outperformed the Milan criteria and UCSF criteria. The CPLCP nomogram combining basic clinical data, pathology data, laboratory data, CT features, and PET metabolic parameters showed good efficacy and high clinical practicability in predicting the early recurrence of HCC after LT.

Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram.A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS).CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort.The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis.

Diffuse hepatic infiltration of lymphoma is extremely rare [1]. Conventional imaging techniques (CT, MR and ultrasound) usually fail to identify the abnormality. F-FDG PET is valuable in detecting diffuse hepatic diseases [2, 3]. A 58-year-old man was admitted to our hospital owing to fever of unknown origin for 2 weeks and jaundice for 10 days. Non-contrast CT images of chest, abdomen and pelvis showed a tender hepatomegaly, splenomegaly and ascites. A hepatic focal or diffuse lesion and enlargement of lymph nodes were not shown.Ultrasound identified hepatomegalywithout any definite hepatic lesions. Magnetic resonance cholangiopancreatography results (MRCP) were negative. Hematology-oncology consultation suggested a possible diffuse malignant infiltration process such as lymphoma or leukemia. Subsequently, a bone marrow biopsy andwhole-body F-FDGPET/CTscanwere performed. There was no evidence of malignancy on bone marrow biopsy. PET/CT images (a–d, Note: The artifact on b & d is because the patient could not lift his arms during the examination) demonstrated diffuse increased F-FDG uptake in the entire liver (“hepatic superscan”). PET/CT images strongly indicated the diagnosis of hepatic lymphoma. Liver biopsy showed a nongerminal center subtype of diffuse large B-cell lymphoma. Immunohistochemical stains (e–g) were positive for CD20 and MUM-1, and negative for CD10 and BCL-6. The patient died 2 days after liver biopsy due to rapid progression of the disease; an autopsy was not performed. This case highlights PET, rather than other imaging techniques, is of great value in detecting diffuse hepatic infiltration of lymphoma with characteristic appearance of Bhepatic superscan^.