Dual-Energy CT Iodine Concentration to Evaluate Postoperative Pancreatic Fistula after Pancreatoduodenectomy.

Background Pancreatic fibrosis and fatty infiltration are associated with postoperative pancreatic fistula (POPF), but accurate preoperative assessment remains a challenge. Iodine concentration (IC) and fat fraction derived from dual-energy CT (DECT) may reflect the amount of fibrosis and steatosis, potentially enabling the preoperative prediction of POPF. Purpose To identify multiphasic DECT-derived IC and fat fraction that improve the prediction of POPF risks compared with contrast-enhanced CT attenuation values and to evaluate the underlying histopathologic changes. Materials and Methods This retrospective study included patients who underwent pancreatoduodenectomy and DECT (including pancreatic parenchymal, portal venous, and delayed phase scanning) between January 2020 and December 2020. The relationships of the quantitative DECT-derived IC and fat fraction, along with CT attenuation values from enhanced images with POPF risk, were analyzed with logistic regression analysis. The predictive performance of the IC was compared with that of the CT values. The histopathologic underpinnings of IC were evaluated with multivariable linear regression analysis. Results A total of 107 patients (median age, 65 years; interquartile range, 57-70 years; 56 men) were included. Of these, 23 (21%) had POPF. The pancreatic parenchymal-to-portal venous phase IC ratio (adjusted odds ratio [OR], 13; 95% CI: 2, 162; P < .001) was an independent predictor of POPF occurrence. The accuracy of the pancreatic parenchymal-to-portal venous phase IC ratio in predicting POPF was higher than that of the CT value ratio in the same phases (78% vs 65%, P < .001). The pancreatic parenchymal-to-portal venous phase IC ratio was independently associated with pancreatic fibrosis (ß = -1.04; 95% CI: -0.44, -1.64; P = .001). Conclusion A higher pancreatic parenchymal-to-portal venous phase IC ratio was associated with less histologic fibrosis and greater risk of POPF. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Yoon in this issue.

Radiomics from Primary Tumor on Dual-Energy CT Derived Iodine Maps can Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.

RATIONALE AND OBJECTIVES: To develop and validate 2 iodine maps based radiomics nomograms for preoperatively predicting cervical lymph node metastasis (LNM) and central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC). MATERIALS AND METHODS: A total of 346 patients with PTC were enrolled and allocated to training (242) and validation (104) sets. Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Aggregated machine-learning strategy was applied for features selection and construction of 2 radiomics scores (LN rad-score; CLN rad-score). Logistic regression model was employed to establish two radiomics nomograms (nomogram 1: predicting LNM; nomogram 2: predicting CLNM) after incorporating LN or CLN rad-score with clinical predictors. Nomograms performance was determined by discrimination, calibration and clinical usefulness. RESULTS: Nomogram 1 incorporated LN rad-score, age (categorized by 55) and CT reported LN status; Nomogram 2 incorporated CLN rad-score, capsule contact >25% and CT reported CLN status. 2 nomograms both showed good discrimination and calibration in the training (AUC = 0.847; AUC = 0.837) and validation cohorts (AUC = 0.807; AUC = 0.795). Significant improved AUC, net reclassification index (NRI) and integrated discriminatory improvement (IDI) confirmed additional great predictive value of 2 rad-scores, compared with clinical models without radiomics. Decision curve analysis indicated clinical utility of nomograms. 2 nomograms both demonstrated favorable predictive efficacy in CT reported LN or CLN negative subgroup (AUC = 0.766; AUC = 0.744). CONCLUSION: The presented 2 radiomics nomograms are useful tools for preoperative prediction of LNM and CLNM in PTC.

Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer.

BACKGROUND: In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). RESULTS: The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. CONCLUSIONS: CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

Feasibility study of using virtual non-contrast images derived from dual-energy CT to replace true non-contrast images in patients diagnosed with papillary thyroid carcinoma.

OBJECTIVE: To assess the feasibility of using virtual non-contrast (VNC) images derived from dual-energy computed tomography (DECT) to replace true non-contrast (TNC) images of papillary thyroid carcinoma (PTC) patients. METHODS: Images of 96 PTC patients were retrospectively analyzed. TNC images were acquired under the single-energy mode of DECT after the plain scanning. The arterial and venous phase VNC (VNC-a and VNC-v) images were generated by the post-processing algorithm from the arterial phase and venous phase of contrast-enhanced CT images, respectively. Mean attenuation values, image noise, number and length of calcification were measured. Radiation dose was also calculated. Last, subjective score of image quality was evaluated by a 5-point scale. RESULTS: Signal-to-noise ratio (SNR) of each tissue in TNC images is significantly higher than that of VNC images (p<0.050). Contrast-to-noise ratio (CNR) of fat, muscle, thyroid nodules and internal carotid artery in TNC images is significantly higher than that of VNC images, while CNR in TNC images is lower for cervical vertebra (p<0.001). Calcification is detected on TNC images of 44 patients, while it is omitted on VNC images of 14 patients (31.8%). The subjective score of TNC images is higher than VNC images (p<0.001). The effective dose reduction is 47.6% by avoiding plain scanning. CONCLUSIONS: Considering the different attenuation value, SNR, CNR and especially reduced detection rate of calcification, we deem that VNC images cannot be directly used to replace TNC images in PTC patients, despite the reduced radiation dose.

Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease.

AIMS: This study was aimed at investigating whether a machine learning (ML)-based coronary computed tomographic angiography (CCTA) derived fractional flow reserve (CT-FFR) SYNTAX score (SS), 'Functional SYNTAX score' (FSSCTA), would predict clinical outcome in patients with three-vessel coronary artery disease (CAD). METHODS AND RESULTS: The SS based on CCTA (SSCTA) and ICA (SSICA) were retrospectively collected in 227 consecutive patients with three-vessel CAD. FSSCTA was calculated by combining the anatomical data with functional data derived from a ML-based CT-FFR assessment. The ability of each score system to predict major adverse cardiac events (MACE) was compared. The difference between revascularization strategies directed by the anatomical SS and FSSCTA was also assessed. Two hundred and twenty-seven patients were divided into two groups according to the SSCTA cut-off value of 22. After determining FSSCTA for each patient, 22.9% of patients (52/227) were reclassified to a low-risk group (FSSCTA ≤ 22). In the low- vs. intermediate-to-high (>22) FSSCTA group, MACE occurred in 3.2% (4/125) vs. 34.3% (35/102), respectively (P < 0.001). The independent predictors of MACE were FSSCTA (OR = 1.21, P = 0.001) and diabetes (OR = 2.35, P = 0.048). FSSCTA demonstrated a better predictive accuracy for MACE compared with SSCTA (AUC: 0.81 vs. 0.75, P = 0.01) and SSICA (0.81 vs. 0.75, P < 0.001). After FSSCTA was revealed, 52 patients initially referred for CABG based on SSCTA would have been changed to PCI. CONCLUSION: Recalculating SS by incorporating lesion-specific ischaemia as determined by ML-based CT-FFR is a better predictor of MACE in patients with three-vessel CAD. Additionally, the use of FSSCTA may alter selected revascularization strategies in these patients.

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma.

BACKGROUND: To investigate whether radiomic features from (18F)-fluorodeoxyglucose positron emission tomography/computed tomography [(18F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent (18F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model's performance. RESULTS: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). CONCLUSIONS: Radiomic features based on (18F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer.

OBJECTIVE: To investigate the value of radiomics analysis of dual-energy computed tomography (DECT)-derived iodine maps for preoperative diagnosing cervical lymph nodes (LNs) metastasis in patients with papillary thyroid cancer (PTC). METHODS: Two hundred and fifty-five LNs (143 non-metastatic and 112 metastatic) were enrolled and allocated to training and validation sets (7:3 ratio). Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 10-fold cross-validation. Logistic regression modeling was employed to build models based on CT image features (model 1), radiomics signature (model 2), and the combined (model 3). A nomogram was plotted for the combined model and decision curve analysis was applied for clinical use. Diagnostic performance was assessed and compared. Internal validation was performed on an independent set containing 78 LNs. RESULTS: Model 3 showed optimal diagnostic performance in both training (AUC = 0.933) and validation set (AUC = 0.895), followed by model 2 (training set, AUC = 0.910; validation set, AUC = 0.847). Both these two models outperformed model 1 in both training (AUC = 0.763) (p < 0.05) and validation set (AUC = 0.728) (p < 0.05). CONCLUSION: Radiomics analysis of DECT-derived iodine maps showed better diagnostic performance than qualitative evaluation of CT image features in preoperative diagnosing cervical LN metastasis in PTC patients. Radiomics signature integrated with CT image features can serve as a promising imaging biomarker for the differentiation. KEY POINTS: • Conventional CT image features have limited value for the diagnosis of metastatic LNs in PTC patients. • Radiomics analysis of dual-energy CT-derived iodine maps significantly outperformed qualitative CT image features in differentiating metastatic from non-metastatic LNs. • Radiomics signature integrated with qualitative CT image features can serve as a useful tool in judging LNs status, thus aiding clinical decision-making.

Stress dynamic myocardial CT perfusion for symptomatic patients with intermediate- or high-risk of coronary artery disease: Optimization and incremental improvement between the absolute and relative myocardial blood flow analysis.

BACKGROUND: The optimization of myocardial CT perfusion (CTP) assessment remains inconsistent and uncertain. Our aim was to explore the superior analysis selection and incremental improvement of myocardial blood flow (MBF) assessment on CTP in diagnosing hemodynamically significant coronary artery disease (CAD). METHODS: Sixty patients (43 men and 17 women; 61.38 ± 8.01 years) were prospectively recruited and underwent stress dynamic myocardial CTP examinations. Absolute and relative MBF was used for ischemia evaluation with the invasive coronary angiography and fractional flow reserve were used as the reference standard. Areas under the receiver operating characteristic curves (AUCs) and cutoff values were calculated and compared. RESULTS: There were 151 vessels in 60 patients finally enrolled for analysis. The sensitivity, specificity, PPV, NPV and diagnostic accuracy for the absolute MBF value and relative MBF ratio were 82.76%, 98.92%, 97.96%, 90.20%, and 92.72% and 74.14%, 93.56%, 87.76%, 85.29%, and 86.09%, respectively. The absolute MBF value was superior than the relative MBF ratio in detecting ischemia (AUC, 0.955 [95%CI: 0.919-0.990] vs.0.906 [95%CI:0.857-0.954])(P = 0.02). For territories with both sensitivity and specificity ≤90%, the diagnostic accuracy increased from 79.1% to 88.4% when the specific data were assessed using the absolute MBF value instead of the relative MBF ratio. CONCLUSIONS: The absolute MBF value from the endocardial myocardium on stress dynamic myocardial CTP showed superior diagnostic performance compared to the relative MBF ratio for the detection of myocardial ischemia in intermediate-to-high risk patients. The absolute MBF value provides an incremental benefit toward diagnostic performance for the relative MBF ratio evaluation.

Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC. METHODS: Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes. RESULTS: Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification. DISCUSSION: The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.