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Simple Summary: The 18F-FDG PET parameters reflecting the intensity and distribution of glucose uptake by the tumor are associated with prognosis in non-small-cell lung cancer (NSCLC) patients. We developed and evaluated an imaging-based model utilizing these 18F-FDG PET-derived features for predicting the five-year survival in NSCLC patients after curative surgery. The PET-based risk score constructed using the LASSO logistic method outperformed the predictive performances of individual 18F-FDG PET parameters. The PET-based risk score was an independent prognostic factor for clinical variables. Additionally, it demonstrated better predictive performance when combined with clinical variables. The FDG PET-based imaging model could aid in risk stratification for personalized adjuvant treatment and follow-up management of NSCLC patients after surgery. The aim of our retrospective study is to develop and assess an imaging-based model utilizing 18F-FDG PET parameters for predicting the five-year survival in non-small-cell lung cancer (NSCLC) patients after curative surgery. A total of 361 NSCLC patients who underwent curative surgery were assigned to the training set (n = 253) and the test set (n = 108). The LASSO regression model was used to construct a PET-based risk score for predicting five-year survival. A hybrid model that combined the PET-based risk score and clinical variables was developed using multivariate logistic regression analysis. The predictive performance was determined by the area under the curve (AUC). The individual features with the best predictive performances were co-occurrence_contrast (AUC = 0.675) and SUL peak (AUC = 0.671). The PET-based risk score was identified as an independent predictor after adjusting for clinical variables (OR 5.231, 95% CI 1.987–6.932; p = 0.009). The hybrid model, which integrated clinical variables, significantly outperformed the PET-based risk score alone in predictive accuracy (AUC = 0.771 vs. 0.696, p = 0.022), a finding that was consistent in the test set. The PET-based risk score, especially when integrated with clinical variables, demonstrates good predictive ability for five-year survival in NSCLC patients following curative surgery. [ABSTRACT FROM AUTHOR]

We investigated the usefulness of quantitative 99mTc-white blood cell (WBC) single photon emission computed tomography (SPECT)/computed tomography (CT) for predicting lower extremity amputation in diabetic foot infection (DFI). A total of 93 feet of 83 consecutive patients with DFI who underwent WBC SPECT/CT for treatment planning were retrospectively analysed. The clinical and SPECT/CT parameters were collected along with the measurements of the maximum standardized uptake value (SUVmax) at DFI. Statistical logistic regression analysis was performed to explore the predictors of LEA and receiver operating characteristic (ROC) curve was analysed to assess the predictive value of SPECT/CT. The independent predictors of amputation were previous amputation (OR 11.9), numbers of SPECT/CT lesions (OR 2.1), and SUVmax of DFI; either continuous SUVmax (1-increase) (OR 1.3) or categorical SUVmax > 1.1 (OR 21.6). However, the conventional SPECT/CT interpretation failed to predict amputation. In ROC analysis, the SUVmax yielded a fair predictor (area under the curve (AUC) 0.782) of amputation. The model developed from these independent predictors yielded an excellent performance for predicting amputation (AUC 0.873). Quantitative WBC SPECT/CT can provide new information useful for predicting the outcomes and guiding treatment for patients with DFI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: The pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) is a surrogate endpoint for predicting long-term clinical benefit in breast cancer. Recently, the use of radiomic features extracted from 18F-FDG PET/CT has emerged as a promising tool for predicting treatment outcomes in various cancers. We developed and externally validated a predictive model using 18F-FDG PET-based radiomics with the least absolute shrinkage and selection operator (LASSO) logistic method for pCR following NAC in breast cancer. Our radiomic-score model demonstrated satisfactory discriminative performances in training, internal validation, and external validation cohorts. Furthermore, the integrated radiomic model incorporating human epidermal growth factor receptor 2 (HER2) status showed improved performance compared to the radiomic-score model alone in all cohorts. The newly developed radiomic-score model might enable a more accurate and personalized assessment of the tumor response to neoadjuvant chemotherapy in breast cancer. 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. [ABSTRACT FROM AUTHOR]

The incomplete democratization in Korea aided by the National Security Law allows antidemocratic violence by political powers in the guise of anticommunist ideology. The need for the powers to do away with the law and make attempts to strengthen democracy and guarantee human rights is emphasized.