We evaluated the diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT surveillance for detecting clinically unsuspected recurrence or second primary cancer (SPC) in patients with non-small cell lung cancer (NSCLC) after curative therapy. A total of 4478 surveillance FDG PET/CT scans from 2864 NSCLC patients without suspicion of recurrence after curative therapy were reviewed retrospectively. In 274 of 2864 (9.6%) patients, recurrent NSCLC or SPC was found by surveillance PET/CT during clinical follow-up. Surveillance PET/CT scans showed sensitivity of 98.9% (274/277), specificity of 98.1% (4122/4201), accuracy of 98.2% (4396/4478), positive predictive value (PPV) of 77.6% (274/353), and negative predictive value of 99.9% (4122/4125). The specificity and accuracy in the curative surgery group were significantly higher than those in the curative radiotherapy group. PPV was significantly improved in subgroups of patients with advanced stage prior to curative therapy, PET/CT scans performed within 3 years after curative-intent therapy, and curative surgery. FDG PET/CT surveillance showed good diagnostic efficacy for detecting clinically unexpected recurrence or SPC in NSCLC patients after curative therapy. It can be more useful when performed soon after therapy in curative surgery recipients and those with an advanced disease stage considering its diagnostic efficacy and yield.

The aim of our retrospective study is to develop and externally validate an ¹⁸F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The ¹⁸F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

Background: The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer. Methods: From 2008 to 2016, data on patients diagnosed with cancer of the larynx and hypopharynx were retrospectively collected. The patients underwent pre- and post-treatment 18F-FDG PET/CT imaging. The values of ΔPre-Post PET were measured from the texture features. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most predictive features to formulate a Rad-score for both progression-free survival (PFS) and overall survival (OS). Kaplan–Meier curve analysis and Cox regression were employed to assess PFS and OS. Then, the concordance index (C-index) and calibration plot were used to evaluate the performance of the radiomics nomogram. Results: Study data were collected for a total of 91 patients. The mean follow-up period was 71.5 mo. (8.4–147.3). The Rad-score was formulated based on the texture parameters and was significantly associated with both PFS (p = 0.024) and OS (p = 0.009). When predicting PFS, only the Rad-score demonstrated a significant association (HR 2.1509, 95% CI [1.100–4.207], p = 0.025). On the other hand, age (HR 1.116, 95% CI [1.041–1.197], p = 0.002) and Rad-score (HR 33.885, 95% CI [2.891–397.175], p = 0.005) exhibited associations with OS. The Rad-score value showed good discrimination when it was combined with clinical parameters in both PFS (C-index 0.802–0.889) and OS (C-index 0.860–0.958). The calibration plots also showed a good agreement between the observed and predicted survival probabilities. Conclusions: Combining clinical parameters with radiomics analysis of pre- and post-treatment 18F-FDG PET/CT parameters in patients with laryngeal cancer and hypopharyngeal cancer might have additional prognostic value.

Objective: To predict the recurrence of non-small cell lung cancer (NSCLC) within 2 years after curative-intent treatment using a machine-learning approach with PET/CT-based radiomics. Patients and methods: A total of 77 NSCLC patients who underwent pretreatment ¹⁸F-fluorodeoxyglucose PET/CT were retrospectively analyzed. Five clinical features (age, sex, tumor stage, tumor histology, and smoking status) and 48 radiomic features extracted from primary tumors on PET were used for binary classifications. These were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with relapsed status. Areas under the receiver operating characteristics curves (AUC) were yielded by six machine-learning algorithms (support vector machine, random forest, neural network, naive Bayes, logistic regression, and gradient boosting). Model performances were compared and validated via random sampling. Results: A PET/CT-based radiomic model was developed and validated for predicting the recurrence of NSCLC during the first 2 years after curation. The most important features were SD and variance of standardized uptake value, followed by low-intensity short-zone emphasis and high-intensity zone emphasis. The naive Bayes model with the 15 best-ranked features displayed the best performance (AUC: 0.816). Prediction models using the five best PET-derived features outperformed those using five clinical variables. Conclusion: The machine learning model using PET-derived radiomic features showed good performance for predicting the recurrence of NSCLC during the first 2 years after a curative intent therapy. PET/CT-based radiomic features may help clinicians improve the risk stratification of relapsed NSCLC.

Spondyloarthritis (SpA) is characterized by inflammatory back pain. Magnetic resonance imaging (MRI) was the earlier gold standard technique for detecting early inflammatory change. We reassessed the diagnostic utility of sacroiliac joint/sacrum (SIS) ratios of single-photon emission computed tomography/computed tomography (SPECT/CT) for identifying sacroiliitis. We aimed to investigate of SPECT/CT in diagnosing SpA using a rheumatologist’s visual scoring of SIS ratios assessment. We conducted a single-center, medical records review study of patients with lower back pain who underwent bone SPECT/CT from August 2016 to April 2020. We employed semiquantitative visual bone scoring methods of SIS ratio. The uptake of each sacroiliac joint was compared to that of the sacrum (0–2). A score of 2 for the sacroiliac joint of either side was considered diagnostic of sacroiliitis. Of the 443 patients assessed, 40 had axial SpA (axSpA), 24 being radiographic axSpA and 16 being nonradiographic axSpA. The sensitivity, specificity, and positive and negative predictive values of SIS ratio of SPECT/CT for axSpA were 87.5%, 56.5%, 16.6%, and 97.8%, respectively. In receiver operating curve analysis, MRI better diagnosed axSpA than did SIS ratio of SPECT/CT. Although the diagnostic utility of SIS ratio of SPECT/CT was inferior to MRI, visual scoring of SPECT/CT affords high sensitivity and negative predictive value in axSpA. When MRI is inappropriate for certain patients, SIS ratio of SPECT/CT is an alternative tool for identifying axSpA in real practice.