This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan–Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.

Sarcopenia negatively affects oncologic outcomes. However, studies have yet to reveal whether it is associated with postoperative complications and survival among patients with oral cavity squamous cell carcinoma (OSCC). This study retrospectively enrolled 592 patients undergoing primary OSCC surgery with available computed tomography (CT) images of their third cervical vertebrae (C3) within 30 days before surgery between January 2011 and December 2020. Preoperative sarcopenia, nutritional and frailty status, tumor characteristics, comorbidities, and inflammatory markers were assessed. The outcome variables included 30-day complications based on the Buzby and Dindo classification, reoperation, 5- and 8-year overall survival, and disease-free survival. A total of 318 (53.7%) had sarcopenia; of these patients, 217 (68.2%) presented with postoperative complications, and 48 (15.1%) underwent reoperations. Sarcopenia and higher systemic immune-inflammatory index were independently associated with local to systemic 30-day complications. Sarcopenia, advanced-stage disease, and extracapsular spread were correlated with 5- and 8-year survival. The presence of sarcopenia is associated with the 30-day complications and short- and long-term survival of patients who had OSCC and underwent surgery.

In this study, we aimed to evaluate the prognostic impact of sarcopenia, five-item modified frailty index (mFI-5), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) in patients with oral cavity squamous cell carcinoma (OSCC) treated with adjuvant chemoradiotherapy (CRT) and their survival outcomes. We retrospectively enrolled 175 patients with OSCC undergoing adjuvant CRT between 2011 and 2018, who were divided into groups with (n = 112) and without (n = 63) sarcopenia. Logistic regression analysis and Cox proportional hazards models were used to determine prognostic factors for CRT-related toxicity, three-year overall survival (OS), and disease-free survival (DFS). Sarcopenia and high PLR were independently associated with CRT-induced anemia (CIA); advanced tumor stage was related to poor three-year OS. CRT and survival did not differ by mFI-5 and NLR. Our results indicate that sarcopenia and high PLR are significant predictors of adjuvant CRT, increasing toxicity outcomes and indicating worse short-term OS. Accurately identifying sarcopenia and high PLR in patients with OSCC is critical to help better select candidates for adjuvant CRT to improve their outcomes.

Radiogenomic heterogeneity features in ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = −0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages (p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.

Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing ¹⁸F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell’s C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.

Purpose: ¹⁸F‐FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on ¹⁸F‐FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical ¹⁸F‐FDG PET images using a data‐driven gating (DDG) algorithm on the digital PET scanner.Materials and Methods: A total of 101 patients who underwent oncological ¹⁸F‐FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. ¹⁸F‐FDG‐avid lesions from the thorax to the upper abdomen were analyzed on the non‐DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first‐order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability.Results: In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most ¹⁸F‐FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first‐order features (entropy), one from the shape features (sphericity), four from the gray‐level co‐occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray‐level run‐length matrix features (run entropy), and 20 from the wavelet filter‐based features.Conclusion: Respiratory motion has a significant impact on ¹⁸F‐FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.