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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: Neoadjuvant chemotherapy is the standard treatment for locally advanced rectal cancer. Preoperative chemoradiotherapy yields clinically significant tumor regression; while some patients exhibit a minimal response, others exhibit a complete pathologic response. We developed deep learning and machine learning models to predict chemoradiotherapy response across external tests using multicenter data. The machine learning model, which used harmonized image features extracted from 18F-FDG PET, showed higher performance and demonstrated reproducibility through external tests compared to the deep learning models using 18F-FDG PET images. Our study highlights the feasibility of predicting the chemoradiotherapy response of individual patients using non-invasive and reliable image feature values. We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using 18F-FDG PET images and harmonized image features extracted from 18F-FDG PET/CT images. Patients diagnosed with pathologic T-stage III rectal cancer with a tumor size > 2 cm were treated with neoadjuvant chemoradiotherapy. Patients with rectal cancer were divided into an internal dataset (n = 116) and an external dataset obtained from a separate institution (n = 40), which were used in the model. AUC was calculated to select image features associated with radiochemotherapy response. In the external test, the machine-learning signature extracted from 18F-FDG PET image features achieved the highest accuracy and AUC value of 0.875 and 0.896. The harmonized first-order radiomics model had a higher efficiency with accuracy and an AUC of 0.771 than the second-order model in the external test. The deep learning model using the balanced dataset showed an accuracy of 0.867 in the internal test but an accuracy of 0.557 in the external test. Deep-learning models using 18F-FDG PET images must be harmonized to demonstrate reproducibility with external data. Harmonized 18F-FDG PET image features as an element of machine learning could help predict chemoradiotherapy responses in external tests with reproducibility. [ABSTRACT FROM AUTHOR]