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.

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.

Effects of 12 weeks of aerobic exercise combined with resistance training on neurocognitive performance in obese women.

Background/Objectives: To the best of our knowledge, there have been no previous studies conducted on the long-term effects of an exercise intervention on deficits in inhibitory control in obese individuals. The aim of this study was thus to examine the effect of 12 weeks of a combination of aerobic and resistance exercise on behavioral and cognitive electrophysiological performance involving cognitive interference inhibition in obese individuals. Methods: Thirty-two qualified healthy obese women were randomly divided into either an exercise group (EG, age: 34.76 ± 5.52 years old; BMI: 29.35 ± 3.52 kg/m2) or a control group (CG, age: 33.84 ± 7.05 years old; BMI: 29.61 ± 4.31 kg/m2). All participants performed the Stroop task, with electrophysiological signals being collected simultaneously before and after a 12-week intervention. The estimated V̇O2max, muscular strength, and body fat percentage (measured with dual-energy X-ray absorptiometry) were also assessed within one week before and after the intervention. Participants in the EG group engaged in 30 min of moderate-intensity aerobic exercise combined with resistance exercise, 5 sessions per week for 12 weeks, while the participants in the CG group maintained their regular lifestyle without engaging in any type of exercise. Results: The results revealed that although a 12-week exercise intervention did not enhance the behavioral indices [e.g., accuracy rates (ARs) and reaction times (RTs)] in the EG group, significantly shorter N2 and P3 latencies and greater P2 and P3 amplitudes were observed. Furthermore, the fat percentage distribution (e.g. total body fat %, trunk fat %, and leg fat %) and level of physical fitness (e.g. estimated V̇O2max and muscular strength) in the EG group were significantly improved. The changes prior to and after the intervention in the P3 amplitude and trunk fat percentage were significantly negatively correlated in the EG group (r = -0.521, p = 0.039). Conclusions: These findings suggested that 12 weeks of aerobic exercise combined with resistance exercise in obese women affects cognitive function broadly, but not specifically in terms of inhibitory control. The percentage of decreased trunk fat may play a potential facilitating role in inhibition processing in obesity.

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study.

Background: Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults. Methods: In a sample of 2,206 male adults aged 17-43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window. Results: The greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70-75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration. Conclusions: This study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.

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.

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.

Metastatic Parathyroid Hormone-Secreting Prostate Adenocarcinoma Mimicking Ectopic Parathyroid Adenoma Demonstrated on 99mTc-MIBI Image.

Tc-MIBI has long been used to localize hyperfunctioning parathyroid tissue in patients with hyperparathyroidism. This tracer can also concentrate in various neoplastic tissues including prostate adenocarcinoma. We herein report a case with parathyroid hormone-secreting metastatic prostate cancer mimicking an ectopic parathyroid adenoma on the Tc-MIBI scan. We conclude that metastatic prostate cancer should be included as one of the differential diagnoses when interpreting Tc-MIBI scan.

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.

Optimal imaging time for Tc-99m phytate lymphoscintigraphy for sentinel lymph node mapping in patients with breast cancer.

OBJECTIVES: Sentinel lymph node (SLN) sampling has become a standard practice in managing early-stage breast cancer. Lymphoscintigraphy is one of the major methods used. The radioactive tracer used in Taiwan is Tc-99m phytate. However, this agent is not commonly used around the world and the optimal imaging time has not been studied. Thus, we investigated the optimal imaging time of Tc-99m phytate lymphoscintigraphy for SLN mapping in patients with breast cancer. MATERIALS AND METHODS: We retrospectively reviewed SLN Tc-99m phytate lymphoscintigraphies in 135 patients with breast cancer between August 2013 and November 2017. The time for the first SLN to be visualized after radiotracer injection was recorded to determine the optimal imaging time. If no SLN was identified on imaging, the scan was continued to 60 min. We also recorded the presurgical technical and clinical factors to analyze the risk factors for nonvisualization of SLN. Each patient's postoperative axillary lymph node status was also recorded. RESULTS: Axillary SLNs were identified on imaging in 94.8% of the patients. All first SLNs presented within 30 min. In 6 of 7 patients with negative imaging, SLNs were identified during surgery using either blue dye or a hand-held gamma probe. Nonvisualization of SLNs on lymphoscintigraphy was significantly associated with a lower injection dose (1.0 mCi vs. 2.0 mCi), 4-injection protocol (compared to 2-injection), and injection around an outer upper quadrant tumor. In addition, patients with axillary lymph node metastasis had a higher percentage of SLN image mapping failure, with a marginally significant difference. CONCLUSION: Based on our study, 30 min after Tc-99m phytate injection is the optimal time for lymphoscintigraphy and delayed imaging beyond 30 min is not necessary. In addition, a lower injection dose, the 4-injection method, and an injection near the outer upper quadrant tumor should be avoided to minimize nonvisualization of SLNs.

Combining the radiomic features and traditional parameters of 18F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery.

OBJECTIVES: To investigate the role of the traditional and radiomic parameters of 18F-FDG PET for predicting the outcomes of patients with esophageal squamous cell carcinoma (SqCC). METHODS: Forty-four patients with primary esophageal SqCC who underwent neoadjuvant chemoradiotherapy (CCRT) followed by esophagectomy (tri-modality treatment) were retrospectively analyzed. All patients underwent 18F-FDG PET/CT before and after neoadjuvant CCRT. The radiomic features were calculated using the pre-treatment PET scan. Pre-treatment radiomic features and changes in the PET-derived traditional parameters after neoadjuvant CCRT were analyzed according to the pathological response to esophagectomy, disease-free survival (DFS), and overall survival (OS). We further developed a scoring system based on the independent survival prognosticators and compared our model to the traditional TNM staging system and surgical pathology. RESULTS: A pre-treatment primary tumor histogram entropy ≥ 3.69 predicts an unfavorable response to neoadjuvant CCRT (OR = 19.25, p = 0.009). An SUVmax reduction ratio ≤ 0.76, a pre-treatment primary tumor code similarity ≤ 0.0235, and incomplete pathological remission were independently associated with poor OS (p = 0.019, 0.033, and 0.038, respectively) and DFS (p = 0.049, 0.021, and 0.009, respectively). The three survival prognosticators were used to construct a scoring system (score 0-1, 2, and 3). Patients with a score of 2 or 3 had a significantly worse survival outcome than those with a score of 0-1 (HRs for OS: 3.58 for score 2, and 15.19 for score 3, p < 0.001; HRs for DFS: 1.39 for score 2 and 6.04 for score 3, p = 0.001).This survival prediction model was superior to the traditional TNM staging system (p < 0.001 versus p = 0.061 for OS, and p = 0.001 versus p = 0.027 for DFS) and the model based on surgical pathology (p < 0.001 versus p = 0.049 for OS, and p = 0.001 versus p = 0.022 for DFS). CONCLUSIONS: The 18F-FDG PET-derived radiomic parameter is useful for predicting the surgical pathological response in patients with esophageal SqCC treated with the tri-modality method. Using a combination of traditional and radiomic PET parameters with clinical profiles enables better stratification of patients into subgroups with various survival rates.

Value of early evaluation of treatment response using 18F-FDG PET/CT parameters and the Epstein-Barr virus DNA load for prediction of outcome in patients with primary nasopharyngeal carcinoma.

PURPOSE: To determine the value of early evaluation of response to concurrent chemoradiotherapy (CCRT) using 18F-FDG PET-derived parameters and the Epstein-Barr virus (EBV) DNA titre in outcome prediction in patients with primary nasopharyngeal carcinoma (NPC). METHODS: Sixty patients with primary NPC were prospectively enrolled. All patients underwent 18F-FDG PET/CT before and during CCRT. The plasma EBV DNA titre was measured along with the PET/CT-derived parameters. Changes in EBV DNA titre and PET/CT-derived parameters during CCRT were analysed in relation to response to treatment, recurrence-free survival (RFS) and overall survival (OS). RESULTS: A total lesion glycolysis (TLG) reduction ratio of ≤0.6 and a detectable EBV DNA titre during CCRT were predictors of an unfavourable response to treatment, RFS and OS. In multivariate analysis, a TLG reduction ratio of ≤0.6 predicted incomplete remission (p = 0.002) and decreased RFS (p = 0.003). The proportion of patients with a TLG reduction ratio of >0.6 who achieved a complete response was more than twice that of patients with a TLG reduction ratio of ≤0.6. A detectable EBV DNA titre, a TLG reduction ratio of ≤0.6 and older age were independently associated with a poorer OS (p = 0.037, 0.009 and 0.016, respectively). A scoring system was developed based on these independent predictors of OS. Patients with a score of 1 and 2/3 had poorer survival outcomes than those with a score of 0 (hazard ratio 4.756, p = 0.074, and hazard ratio 18.973, p = 0.001, respectively). This scoring system appeared to be superior to the traditional TNM staging system (p < 0.001 versus p = 0.175). CONCLUSION: Early evaluation of response to CCRT using 18F-FDG PET-derived parameters and the EBV DNA titre can predict outcome in patients with primary NPC. A combination of interim PET parameters and the EBV DNA titre enables better stratification of patients into subgroups with different survival rates.

Low-dose computed tomography scans with automatic exposure control for patients of different ages undergoing cardiac PET/CT and SPECT/CT.

PURPOSE: This study aimed to evaluate the efficacy of automatic exposure control (AEC) in order to optimize low-dose computed tomography (CT) protocols for patients of different ages undergoing cardiac PET/CT and single-photon emission computed tomography/computed tomography (SPECT/CT). METHODS: One PET/CT and one SPECT/CT were used to acquire CT images for four anthropomorphic phantoms representative of 1-year-old, 5-year-old and 10-year-old children and an adult. For the hybrid systems investigated in this study, the radiation dose and image quality of cardiac CT scans performed with AEC activated depend mainly on the selection of a predefined image quality index. Multiple linear regression methods were used to analyse image data from anthropomorphic phantom studies to investigate the effects of body size and predefined image quality index on CT radiation dose in cardiac PET/CT and SPECT/CT scans. RESULTS: The regression relationships have a coefficient of determination larger than 0.9, indicating a good fit to the data. According to the regression models, low-dose protocols using the AEC technique were optimized for patients of different ages. In comparison with the standard protocol with AEC activated for adult cardiac examinations used in our clinical routine practice, the optimized paediatric protocols in PET/CT allow 32.2, 63.7 and 79.2% CT dose reductions for anthropomorphic phantoms simulating 10-year-old, 5-year-old and 1-year-old children, respectively. The corresponding results for cardiac SPECT/CT are 8.4, 51.5 and 72.7%. CONCLUSION: AEC is a practical way to reduce CT radiation dose in cardiac PET/CT and SPECT/CT, but the AEC settings should be determined properly for optimal effect. Our results show that AEC does not eliminate the need for paediatric protocols and CT examinations using the AEC technique should be optimized for paediatric patients to reduce the radiation dose as low as reasonably achievable.