We evaluated the prognostic value of volume-based metabolic positron emission tomography (PET) parameters in patients with small cell lung cancer (SCLC) compared with other factors. The subjects were 202 patients with pathologically proven SCLC who underwent pretreatment 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT). Volumetric metabolic parameters of intrathoracic malignant hypermetabolic lesions, including maximum and average standardized uptake value, sum of metabolic tumor volume (MTV), and sum of total lesion glycolysis (TLG) were measured. 164 patients had died during follow-up (median 17.4 months) and median overall survival was 14 months. On univariate survival analysis, age, stage, treatment modality, sum of MTV (cutoff = 100 cm3), and sum of TLG (cutoff = 555) were significant predictors of survival. There was a very high correlation between the sum of MTV and the sum of TLG (r = 0.963, P < 0.001). On multivariate survival analysis, age (HR = 1.04, P < 0.001), stage (HR = 2.442, P < 0.001), and sum of MTV (HR = 1.662, P = 0.002) were independent prognostic factors. On subgroup analysis based on limited disease (LD) and extensive disease (ED), sum of MTV and sum of TLG were significant prognostic factors only in LD. Both sum of MTV and sum of TLG of intrathoracic malignant hypermetabolic lesions are important independent prognostic factors for survival in patients with SCLC, in addition to age and clinical stage. However, it may be more useful in limited disease rather than in extensive disease.

We investigated predictions from ¹⁸F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent ¹⁸F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

We are aimed to evaluate the diagnostic performances of quantitative indices obtained from dual-phase ¹⁸F-FP-CIT PET/CT for differential diagnosis of atypical parkinsonian syndromes (APS) from Parkinson’s disease (PD). We analyzed 172 subjects, including 105 non-Parkinsonism, 26 PD, 8 PSP, 1 CBD, 8 MSA-P, 9 MSA-C, and 15 DLB retrospectively. Two sequential PET/CT scans were acquired at 5 min and 3 h. We compared subregional binding potentials, putamen-to-caudate nucleus ratio of the binding potential, asymmetry index, and degree of washout. To differentiate APS, all BPs in both early and late phases (except late BPbrainstem) and all factors of the percent change except for putamen in APS significantly differed from PD. When a cut-off for early BPcerebellum was set as 0.79, the sensitivity, specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy for differentiating APS 73.2%, 91.7%, 93.8%, 66.7%, and 80.0%. The early BPcerebellum showed significantly greater SP and PPV than the late quantitative indices. Combined criteria regarding both early and late indices exhibited only greater NPV. The quantitative indices showed high diagnostic performances in differentiating APS from PD. Our findings provide the dual-phase ¹⁸F-FP-CIT PET/CT would be useful for differentiating APS from PD.

This study aimed to evaluate the diagnostic performance of three-phase bone scintigraphy (TPBS) and digital infrared thermography imaging (DITI) in the chronic post-traumatic CRPS and propose new imaging diagnostic criteria that combine the two tests. We retrospectively enrolled 44 patients with suspected symptoms of CRPS from various injuries during obligatory military service. We analyzed the following findings: (1) uptake pattern on TPBS, (2) uptake ratios of affected and unaffected sides in each phase of TPBS, (3) difference in body skin temperature on DITI. New criteria combining the above findings were also evaluated. Eighteen patients were finally defined as CRPS according to the Budapest criteria. Uptake pattern and uptake ratio in blood pool phase on the TPBS were significantly different between CRPS and non-CRPS groups (both p < 0.05). The DITI could not discriminate significantly between the groups (p = 0.334). The diagnostic criteria considering both the pattern analysis and quantitative analysis in TPBS exhibited the highest positive likelihood ratio. On the other hand, the diagnostic criteria combining DITI and TPBS showed the lowest negative likelihood ratio value. TPBS can be useful in diagnosing chronic post-traumatic CRPS. Moreover, we can suggest that different diagnostic criteria be applied depending on the purpose.

We investigated the prognostic significance of radiomic features from ¹⁸F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. We enrolled 300 patients with stage III NSCLC who underwent PET/CT at the initial work-up (PET1) and after neoadjuvant concurrent chemoradiotherapy (PET2). Radiomic primary tumor features were subjected to LASSO regression to select the most useful prognostic features of OS. The prognostic significance of the LASSO score and conventional PET parameters was assessed by Cox proportional hazards regression analysis. In conventional PET parameters, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of each PET1 and PET2 were significantly associated with OS. In addition, both the PET1-LASSO score and the PET2-LASSO score were significantly associated with OS. In multivariate Cox regression analysis, only the PET2-LASSO score was an independently significant factor for OS. The LASSO score showed better predictive performance for OS regarding the time-dependent receiver operating characteristic curve and decision curve analysis than conventional PET parameters. Radiomic features from PET/CT were an independent prognostic factor for the estimation of OS in stage III NSCLC. The newly developed LASSO score using radiomic features showed better prognostic results for individualized OS estimation than conventional PET parameters.

We report a case of dialysis-related amyloid arthropathy in a patient with end-stage renal disease. It presented in our patient as moderately increased FDG uptake in the amyloid deposition in the periarticular tissues and eroding into adjacent bones.