Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.

OBJECTIVE: To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. METHODS: We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. RESULTS: In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). CONCLUSIONS: Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

PURPOSE: 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-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 18 F-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. 18 F-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 18 F-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 18 F-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.

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study.

OBJECTIVE: The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma. METHODS: We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation. RESULTS: In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort. CONCLUSION: Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.

Comprehensive Assessment of the Clinical Risk Factors of Postoperative Adverse Events and Survival in Patients With Non-small-cell Lung Cancer.

BACKGROUND/AIM: Postoperative adverse events are associated with poor clinical outcomes and survival in patients with non-small-cell lung cancer (NSCLC) treated with curative operation. However, comprehensive evaluation of the clinical characteristics associated with postoperative adverse events and survival outcomes is lacking. PATIENTS AND METHODS: A retrospective study that evaluated patients with NSCLC who underwent curative surgery between 2008 and 2019 was conducted in a medical center. The baseline characteristics, five-item modified frailty index, sarcopenia, inflammatory biomarkers, surgical approach, postoperative adverse events, and survival were statistically analyzed. RESULTS: Patients with a history of smoking and preoperative sarcopenia were at a higher risk of developing postoperative pulmonary complications. Smoking, frailty, and traditional open thoracotomy (OT) were associated with infections, and sarcopenia was identified as a risk factor for major complications. Advanced tumor stage, high neutrophil-to-lymphocyte ratio, OT, major complications, and infections were identified as risk factors for overall and disease-free survival. CONCLUSION: Pre-treatment sarcopenia was found to be a predictor of major complications. Infections and major complications were associated with survival outcomes in patients with NSCLC.

Association Between the Neutrophil-to-Lymphocyte Ratio and Infection and Survival in Diffuse Large B Cell Lymphoma.

BACKGROUND/AIM: Infection is a common cause of morbidity and mortality in patients treated for diffuse large B-cell lymphoma (DLBCL). However, there is limited information on the impact and risk factors for infection among patients receiving rituximab, cyclophosphamide, vincristine, doxorubicin, and prednisolone (R-CHOP). PATIENTS AND METHODS: A retrospective study evaluating patients with DLBCL receiving R-CHOP and R-COP between 2004 and 2021 was conducted at a medical center. Hospital patients' records for the five-item modified frailty index (mFI-5), sarcopenia, blood-based inflammatory markers, and clinical outcomes were statistically analyzed. RESULTS: Patients with frailty, sarcopenia, and high neutrophil-to-lymphocyte ratio (NLR) were associated with a higher risk of infections. The revised International Prognostic Index poor-risk group, high NLR, infections, and treatment modality were risk factors for shorter progression-free and overall survival. CONCLUSION: Pre-treatment high NLR was a predictor of infection and survival outcome in DLBCL patients.

Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer.

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.

Influence of the methodological aspects of the dichotomization of total metabolic tumor volume measured through baseline fluorine-18 fluorodeoxyglucose PET on survival prediction in lymphoma.

OBJECTIVE: The total metabolic tumor volume (TMTV) measured from fluorine-18 fluorodeoxyglucose (18F-FDG) PET can be useful for determining the prognosis of patients with lymphoma. Stratifying patients into high- and low-TMTV risk groups requires a cutoff point, which is determined through the dichotomization method. This study investigated whether different TMTV dichotomization methods influenced survival prediction in patients with lymphoma. METHODS: We retrospectively enrolled 129 patients with lymphoma who had undergone baseline 18F-FDG PET. TMTV was calculated using a fixed standardized uptake value threshold of 4.0. A total of six methods were employed to determine the optimal TMTV cutoff point using receiver-operating characteristic curve analyses, X-Tile bioinformatics software, and the Cutoff Finder web application. The prognostic performance of each method in survival prediction was examined. RESULTS: The median (interquartile range) TMTV was 123 cm3 (21-335 cm3). The optimal TMTV cutoff values for predicting progression-free survival (PFS) and overall survival (OS) were in the range of 144-748 cm3. The cutoff points were used to dichotomize patients into two groups with distinct prognoses. All TMTV dichotomizations were significantly predictive of PFS and OS. The survival curves showed significant differences between the high- and low-TMTV groups. The C-indices of the survival models did not significantly differ in any of the dichotomizations. CONCLUSION: The prognostic significance of TMTV was maintained regardless of the methodological aspects of dichotomization. However, the optimal TMTV cutoff point varied according to the chosen dichotomization method. Care should be taken when establishing an optimal TMTV cutoff point for clinical use.

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma.

OBJECTIVE: We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma. METHODS: We retrospectively analyzed the 18F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models. RESULTS: Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively). CONCLUSION: The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease.

OBJECTIVE: To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease. METHODS: We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival. RESULTS: Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies. CONCLUSION: Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

Association between Sarcopenia and Immediate Complications and Mortality in Patients with Oral Cavity Squamous Cell Carcinoma Undergoing Surgery.

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.

Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy.

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.

Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma.

OBJECTIVE: The diagnostic performance of 18F-FDG PET for detecting regional lymph node metastasis in resectable lung cancer is variable, and its sensitivity for adenocarcinoma is even lower. We aimed to evaluate the value of 18F-FDG PET-derived features in predicting pathological lymph node metastasis in patients with lung adenocarcinoma. METHODS: We retrospectively analyzed pretreatment 18F-FDG PET-derived features of 126 lung adenocarcinoma patients who underwent curative surgery. A logistic regression model was used to analyze the association between study variables and pathological regional lymph node status obtained from the curative surgery. Furthermore, Cox regression analysis was used to test the effect of the study variables on survival outcomes, including disease-free survival (DFS) and overall survival (OS). RESULTS: The primary tumor entropy (OR = 1.7, p = 0.014) and visual interpretation of regional nodes via 18F-FDG PET (OR = 2.5, p = 0.026) independently predicted pathological regional lymph node metastasis. The areas under the receiver-operating-characteristic curves were 0.631, 0.671, and 0.711 for visual interpretation, primary tumor entropy, and their combination, respectively. Based on visual interpretation, a primary tumor entropy ≥ 3.0 improved the positive predictive value of positive visual interpretation from 51.2% to 63.0%, whereas an entropy < 3.0 improved the negative predictive value of negative visual interpretation from 75.3% to 82.6%. In cases with positive visual interpretation and low entropy, or negative visual interpretation and high entropy, the nodal metastasis rates were approximately 30%. In the survival analyses, the primary tumor entropy was also independently associated with DFS (HR = 2.7, p = 0.001) and OS (HR = 4.8, p = 0.001). CONCLUSIONS: Our preliminary results show that the primary tumor entropy may improve 18F-FDG PET visual interpretation in predicting pathological nodal metastasis in lung adenocarcinoma, and may also show a survival prognostic value. This versatile biomarker may facilitate tailored therapeutic strategies for patients with resectable lung adenocarcinoma.

Evaluation of Sarcopenia, Frailty, and Inflammation on Adverse Events and Survival Outcomes in Patients with Oral Cavity Squamous Cell Carcinoma under Adjuvant Chemoradiotherapy.

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.

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic-Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis.

We investigated whether the combination of primary tumor and nodal 18F-FDG PET parameters predict survival outcomes in patients with nodal metastatic non-small cell lung cancer (NSCLC) without distant metastasis. We retrospectively extracted pre-treatment 18F-FDG PET parameters from 89 nodal-positive NSCLC patients (stage IIB-IIIC). The Cox proportional hazard model was used to identify independent prognosticators of overall survival (OS) and progression-free survival (PFS). We devised survival stratification models based on the independent prognosticators and compared the model to the American Joint Committee on Cancer (AJCC) staging system using Harrell's concordance index (c-index). Our results demonstrated that total TLG (the combination of primary tumor and nodal total lesion glycolysis) and age were independent risk factors for unfavorable OS (p < 0.001 and p = 0.001) and PFS (both p < 0.001), while the Eastern Cooperative Oncology Group scale independently predicted poor OS (p = 0.022). Our models based on the independent prognosticators outperformed the AJCC staging system (c-index = 0.732 versus 0.544 for OS and c-index = 0.672 versus 0.521 for PFS, both p < 0.001). Our results indicate that incorporating total TLG with clinical factors may refine risk stratification in nodal metastatic NSCLC patients and may facilitate tailored therapeutic strategies in this patient group.

Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma.

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, RLNGLRLM, 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.

Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma.

BACKGROUND: To investigate the survival prognostic value of the radiomic features of 18F-FDG PET in patients who had EGFR (epidermal growth factor receptor) mutated lung adenocarcinoma and received targeted TKI (tyrosine kinase inhibitor) treatment. METHODS: Fifty-one patients with stage III-IV lung adenocarcinoma and actionable EGFR mutation who received first-line TKI were retrospectively analyzed. All patients underwent pretreatment 18F-FDG PET/CT, and we calculated the PET-derived radiomic features. Cox proportional hazard model was used to examine the association between the radiomic features and the survival outcomes, including progression-free survival (PFS) and overall survival (OS). A score model was established according to the independent prognostic predictors and we compared this model to the TNM staging system using Harrell's concordance index (c-index). RESULTS: Forty-eight patients (94.1%) experienced disease progression and 41 patients (80.4%) died. Primary tumor SUV entropy > 5.36, and presence of pleural effusion were independently associated with worse OS (both p < 0.001) and PFS (p = 0.001, and 0.003, respectively). We used these two survival predictors to devise a scoring system (score 0-2). Patients with a score of 1 or 2 had a worse survival than those with a score of 0 (HR for OS: 3.6, p = 0.006 for score 1, and HR: 21.8, p < 0.001 for score 2; HR for PFS: 2.2, p = 0.027 for score 1 and HR: 8.8, p < 0.001 for score 2). Our scoring system surpassed the TNM staging system (c-index = 0.691 versus 0.574, p = 0.013 for OS, and c-index = 0.649 versus 0.517, p = 0.004 for PFS). CONCLUSIONS: In this preliminary study, combining PET radiomics with clinical risk factors may improve survival stratification in stage III-IV lung adenocarcinoma with actionable EFGR mutation. Our proposed scoring system may assist with optimization of individualized treatment strategies in these patients.

Preoperative F-18 fluorocholine PET/CT for the detection of hyperfunctioning parathyroid glands in patients with secondary or tertiary hyperparathyroidism: comparison with Tc-99m sestamibi scan and neck ultrasound.

OBJECTIVES: Currently, neck ultrasound is the preferred preoperative imaging in patients with secondary/tertiary hyperparathyroidism, and the use of Tc-99m sestamibi scan is limited in these patients. We conducted this study to compare the diagnostic utilities of F-18 fluorocholine PET/CT, Tc-99m sestamibi scintigraphy, and neck ultrasound for localizing hyperfunctioning parathyroid glands in secondary/tertiary hyperparathyroidism. METHODS: We prospectively enrolled 30 dialysis patients with a diagnosis of secondary/tertiary hyperparathyroidism; of these, 27 participants underwent all three imaging modalities, including dual-phase F-18 fluorocholine PET/CT (PET acquired 5 and 60 min after tracer injection), dual-phase Tc-99 m sestamibi SPECT/CT, and neck ultrasound. All patients underwent parathyroidectomy after imaging. We compared the lesion-based sensitivity, specificity, and accuracy of the three image tools using histopathology as the reference. RESULTS: A total of 27 patients (107 lesions) underwent all three imaging modalities and entered the final analysis. The lesion-based sensitivities of F-18 fluorocholine PET/CT, Tc-99m sestamibi, and ultrasound were 86%, 55%, and 62%, respectively (both p < 0.001, when comparing F-18 fluorocholine PET/CT to Tc-99 m sestamibi scan and to ultrasound). F-18 fluorocholine PET/CT, Tc-99m sestamibi, and ultrasound had similar specificities of 93%, 80%, and 87%, respectively. The accuracy of F-18 fluorocholine PET/CT (87%) was significantly higher than that of Tc-99m sestamibi (59%) and ultrasound (65%) (both p < 0.001). F-18 fluorocholine PET/CT identified more hyperplastic glands than ultrasound in 52% (14/27) patients. The sensitivity of F-18 fluorocholine PET/CT reached 95% for hyperplastic parathyroid masses as low as 200 mg. CONCLUSIONS: F-18 fluorocholine PET/CT shows superior accuracy over the conventional imaging modalities in patients with secondary or tertiary hyperparathyroidism. The combination of F-18 fluorocholine PET/CT and neck ultrasound may enable better surgical planning in these patients. REGISTRATION IDENTIFICATION NUMBER: NCT04316845.

Association Between Sarcopenia and Clinical Outcomes in Patients With Esophageal Cancer Under Neoadjuvant Therapy.

BACKGROUND/AIM: This study aimed to evaluate the association of sarcopenia and Clinical Outcomes with esophageal cancer under neoadjuvant chemoradiotherapy (CRT). PATIENTS AND METHODS: A retrospective study assessing patients with esophageal cancer who underwent CRT between 2001 and 2014 was conducted in the medical center. Hospital patients' records on sarcopenia and treatment outcomes were statistically analyzed. RESULTS: The sarcopenia group had significantly lower body mass index than the non-sarcopenia group. CRT-related severe adverse events with mucositis, fever, and neutropenic fever were greater in the sarcopenia group. Overall survival and disease-free survival were significantly better in the non-sarcopenia group. Sarcopenic patients who received nutritional support with enteral access had less severe mucositis. There was no difference in mortality of sarcopenia patients with nutritional support via enteral access or without. Moreover, sarcopenia and advanced tumor stage were independent factors for mortality outcome. CONCLUSION: Sarcopenia before CRT may be associated with increased toxicities and worse overall survival/ disease-free survival in esophageal cancer patients.

Intratumor Heterogeneity Assessed by 18F-FDG PET/CT Predicts Treatment Response and Survival Outcomes in Patients with Hodgkin Lymphoma.

RATIONALE AND OBJECTIVES: Radiomic analysis of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images enables the extraction of quantitative information of intratumour heterogeneity. This study investigated whether the baseline 18F-FDG PET/CT radiomics can predict treatment response and survival outcomes in patients with Hodgkin lymphoma (HL). MATERIALS AND METHODS: Thirty-five patients diagnosed with HL who underwent 18F-FDG PET/CT scans before and during chemotherapy were retrospectively enrolled in this investigation. For each patient, we extracted 709 radiomic features from pretreatment PET/CT images. Clinical variables (age, gender, B symptoms, bulky tumor, and disease stage) and radiomic signatures (intensity, texture, and wavelet) were analyzed according to response to therapy, progression-free survival (PFS), and overall survival (OS). Receiver operating characteristic curve, logistic regression, and Cox proportional hazards model were used to examine potential predictive and prognostic factors. RESULTS: High-intensity run emphasis (HIR) of PET and run-length nonuniformity (RLNU) of CT extracted from gray-level run-length matrix (GLRM) in high-frequency wavelets were independent predictive factors for the treatment response (odds ratio [OR] = 36.4, p = 0.014; OR = 30.4, p = 0.020). Intensity nonuniformity (INU) of PET and wavelet short run emphasis (SRE) of CT from GLRM and Ann Arbor stage were independently related to PFS (hazard ratio [HR] = 9.29, p = 0.023; HR = 18.40, p = 0.012; HR = 7.46, p = 0.049). Zone-size nonuniformity (ZSNU) of PET from gray-level size zone matrix (GLSZM) was independently associated with OS (HR = 41.02, p = 0.001). Based on these factors, a prognostic stratification model was devised for the risk stratification of patients. The proposed model allowed the identification of four risk groups for PFS and OS (p < 0.001 and p < 0.001). CONCLUSION: HIR_GLRMPET and RLNU_GLRMCT in high-frequency wavelets serve as independent predictive factors for treatment response. ZSNU_GLSZMPET, INU_GLRMPET, and wavelet SRE_GLRMCT serve as independent prognostic factors for survival outcomes. The present study proposes a prognostic stratification model that may be clinically beneficial in guiding risk-adapted treatment strategies for patients with HL.

Prognostic Value of Pretreatment Radiomic Features of 18F-FDG PET in Patients With Hodgkin Lymphoma.

PURPOSE: This study investigated whether a radiomic analysis of pretreatment F-FDG PET can predict prognosis in patients with Hodgkin lymphoma (HL). METHODS: Forty-two patients who were diagnosed as having HL and underwent pretreatment F-FDG PET scans were retrospectively enrolled. For each patient, we extracted 450 radiomic features from PET images. The prognostic significance of the clinical and radiomic features was assessed in relation to progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curve, Cox proportional hazards regression, and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate the predictive value. RESULTS: Intensity nonuniformity extracted from a gray-level run-length matrix and the Ann Arbor stage were independently associated with PFS (hazard ratio [HR] = 22.8, P < 0.001; HR = 7.6, P = 0.024) and OS (HR = 14.5, P = 0.012; HR = 8.5, P = 0.048), respectively. In addition, SUV kurtosis was an independent prognosticator for PFS (HR = 6.6, P = 0.026). We devised a prognostic scoring system based on these 3 risk predictors. The proposed scoring system further improved the risk stratification of the current staging classification (P < 0.001). CONCLUSIONS: The radiomic feature intensity nonuniformity is an independent prognostic predictor of PFS and OS in patients with HL. We devised a prognostic scoring system, which may be more beneficial for patient risk stratification in guiding therapy compared with the current Ann Arbor staging system.