Trimodality therapy for locally advanced esophageal squamous cell carcinoma: the role of volume-based PET/CT in patient management and prognostication.

PURPOSE: To evaluate the role of positron emission tomography/computed tomography (PET/CT) in predicting pathologic complete response (pCR) and identify relevant prognostic factors from clinico-imaging-pathologic features of locally advanced esophageal squamous cell carcinoma (eSCC) patients undergoing trimodality therapy. METHODS: We evaluated 275 patients with eSCCs of T3-T4aN0M0 and T1-T4aN1-N3M0 who received trimodality therapy. We correlated volume-based PET/CT parameters before and after concurrent chemoradiation therapy with pCR after surgery, clinico-imaging-pathologic features, and patient survival. RESULTS: pCR occurred in 75 (27.3%) of 275 patients, of whom 61 (80.9%) showed 5-year survival. Pre-total lesion glycolysis (pre-TLG, OR = 0.318, 95% CI 0.169 to 0.600), post-metabolic tumor volume (post-MTV, OR = 0.572, 95% CI 0.327 to 0.999), and % decrease of average standardized uptake value (% SUVavg decrease, OR = 2.976, 95% CI = 1.608 to 5.507) were significant predictors for pCR. Among them, best predictor for pCR was pre-TLG with best cutoff value of 205.67 and with AUC value of 0.591. Performance status (HR = 5.171, 95% CI 1.737 to 15.397), pathologic tumor size (HR = 1.645, 95% CI 1.351 to 2.002), pathologic N status (N1, HR = 1.572, 95% CI 1.010 to 2.446; N2, HR = 3.088, 95% CI 1.845 to 5.166), and post-metabolic tumor volume (HR = 1.506, 95% CI 1.033 to 2.195) were significant predictors of overall survival. CONCLUSION: Pre-TLG, post-MTV, and % SUVavg decrease are predictive of pCR. Additionally, several clinico-imaging-pathologic factors are significant survival predictors in locally advanced eSCC patients undergoing trimodality therapy.

Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors.

Background: Enrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through computed tomography (CT) radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we assess TIL enrichment objectively using an artificial intelligence-powered TIL analysis in hematoxylin and eosin (H&E) image and analyze its association with quantitative radiomic features (RFs). Clinical significance of the selected RFs is then validated in the independent NSCLC patients who received ICI. Methods: In the training cohort containing both tumor tissue samples and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. The TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density divided by the whole TME area, as measured on an H&E slide. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was applied to CT images from the validation cohort, which included NSCLC patients who received ICI monotherapy. Results: A total of 220 NSCLC samples were included in the training cohort. After filtering the RFs, two features, gray level variance (coefficient 1.71 x 10-3) and large area low gray level emphasis (coefficient -2.48 x 10-5), were included in the model. The two features were both computed from the size-zone matrix, which has strength in reflecting intralesional texture heterogeneity. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared to those with low predicted TILes (median 4.0 months [95% CI 2.2-5.7] versus 2.1 months [95% CI 1.6-3.1], p = 0.002). Patients who experienced a response to ICI or stable disease with ICI had higher predicted TILes compared with the patients who experienced progressive disease as the best response (p = 0.001, p = 0.036, respectively). Predicted TILes was significantly associated with progression-free survival independent of PD-L1 status. Conclusions: In this CT radiomics model, predicted TILes was significantly associated with ICI outcomes in NSCLC patients. Analyzing TME through radiomics may overcome the limitations of tissue-based analysis and assist clinical decisions regarding ICI.

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.

We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal-training and internal-validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16-2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.

Rethinking a Non-Predominant Pattern in Invasive Lung Adenocarcinoma: Prognostic Dissection Focusing on a High-Grade Pattern.

BACKGROUND: Prognostic considerations for non-predominant patterns are necessary because most lung adenocarcinomas (ADCs) have a mixed histologic pattern, and the spectrum of actual prognosis varies widely even among lung ADCs with the same most predominant pattern. We aimed to identify prognostic stratification by second most predominant pattern of lung ADC and to more accurately assess prognostic factors with CT imaging analysis, particularly enhancing non-predominant but high-grade pattern. METHODS: In this prospective study, patients with early-stage lung ADC undergoing curative surgery underwent preoperative dual-energy CT (DECT) and positron emission tomography (PET)/CT. Histopathology of ADC, the most predominant and second most predominant histologic patterns, and preoperative imaging parameters were assessed and correlated with patient survival. RESULTS: Among the 290 lung ADCs included in the study, 231 (79.7%) were mixed-pathologic pattern. When the most predominant histologic pattern was intermediate-grade, survival curves were significantly different among the three second most predominant subgroups (p = 0.004; low, lepidic; intermediate, acinar and papillary; high, micropapillary and solid). When the second most predominant pattern was high-grade, recurrence risk increased by 4.2-fold compared with the low-grade group (p = 0.005). To predict a non-predominant but high-grade pattern, the non-contrast CT value of tumor was meaningful with a lower HU value associated with the histologic combination of lower grade (low-grade as most predominant and intermediate-grade as second most predominant pattern, OR = 6.15, p = 0.005; intermediate-grade as most predominant and high-grade as second most predominant pattern, OR = 0.10, p = 0.033). SUVmax of the tumor was associated with the non-predominant but high-grade pattern, especially in the histologic combination of intermediate-high grade (OR = 1.14, p = 0.012). CONCLUSIONS: The second most predominant histologic pattern can stratify lung ADC patients according to prognosis. Thus, predicting the malignant potential and establishing treatment policies should not rely only on the most predominant pattern. Moreover, imaging parameters of non-contrast CT value and SUVmax could be useful in predicting a non-predominant but high-grade histologic pattern.

Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients.

INTRODUCTION: To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). METHODS: We included 85 patients (M:F = 71:14; age, 35-88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. RESULTS: Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). CONCLUSION: In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

Clinical T category for lung cancer staging: A pragmatic approach for real-world practice.

BACKGROUND: To determine which components should be measured and which window settings are appropriate for computerized tomography (CT) size measurements of lung adenocarcinoma (ADC) and to explore interobserver agreement and accuracy according to the eighth edition of TNM staging. METHODS: A total of 165 patients with surgically resected lung ADC earlier than stage 3A were included in this study. One radiologist and two pulmonologists independently measured the total and solid sizes of components of tumors on different window settings and assessed solidity. CT measurements were compared with pathologic size measurements. RESULTS: In categorizing solidity, 25% of the cases showed discordant results among observers. Measuring the total size of a lung adenocarcinoma predicted pathologic invasive components to a degree similar to measuring the solid component. Lung windows were more accurate (intraclass correlation [ICC] = 0.65-0.81) than mediastinal windows (ICC = 0.20-0.72) at predicting pathologic invasive components, especially in a part-solid nodule. Interobserver agreements for measurement of solid components were good with little significant difference (lung windows, ICC = 0.89; mediastinal windows, ICC = 0.91). A high level of interobserver agreement was seen between the radiologist and pulmonologists and between residents (from the division of pulmonology and critical care) versus a fellow (from the division of pulmonology and critical care) on different windows. CONCLUSIONS: A considerable percentage (25%) of discrepancies was encountered in categorizing the solidity of lesions, which may decrease the accuracy of measurements. Lung window settings may be superior to mediastinal windows for measuring lung ADCs, with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Lung window settings are better for evaluating part-solid lung adenocarcinoma (ADC), with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. The considerable percentage (25%) of discrepancies in categorizing solidity of the lesions may also have decreased the accuracy of measurements. WHAT THIS STUDY ADDS: For accurate measurement and categorization of lung ADC, robust quantitative analysis is needed rather than a simple visual assessment.

Nasal eosinophilia and eosinophil peroxidase in children and adolescents with rhinitis.

BACKGROUND: Researchers have shown that eosinophil peroxidase (EPO) is a relatively accurate marker of eosinophilia and eosinophil activity. However, its use as a marker of eosinophilic inflammation in nasal secretions is limited because the diagnostic cutoff values of EPO for use as a one-time test for allergic diseases such as allergic rhinitis have not been established. PURPOSE: To identify the correlation between nasal eosinophil count and EPO in children and adolescents with rhinitis. METHODS: We recruited patients <18 years of age with rhinitis for more than 2 weeks or more than 2 episodes a year whose nasal eosinophil and EPO were measured at a single allergy clinic. The eosinophil percentage was calculated by dividing the eosinophil count by the number of total cells under light microscopy at ×1,000 magnification. EPO and protein were measured from nasal secretions. We retrospectively analyzed the correlation between nasal eosinophils and protein-corrected EPO (EPO/protein) value. RESULTS: Of the 67 patients enrolled, 41 were male (61.2%); the mean age was 8.2±4.0 years. The median nasal eosinophil count was 1 and percentage was 1%. The median protein-corrected EPO value was 12.5 ng/µg (range, 0-31 ng/µg). There was a statistically significant correlation between eosinophil count and percentage (P<0.001). However, the eosinophil percentage and EPO did not correlate. The eosinophil count and EPO had a statistically significant correlation (P =0.01). The EPO cutoff value examined for nasal eosinophil counts of 2, 5, 10, and 20 was 17.57 ng/µg regardless of the reference count. The largest area under the curve value was obtained when the receiver operating characteristic curve was drawn using the eosinophil count of 2. CONCLUSION: Nasal eosinophil count was significantly associated with protein-corrected EPO.

Unilateral Lung Involvement of Nodular Bronchiectatic Mycobacterium Avium Complex Pulmonary Diseases: Proportion and Evolution on Serial CT Studies.

OBJECTIVE. We aimed to explore proportion, involved organisms, and serial CT features of nodular bronchiectatic (NB) Mycobacterium avium complex (MAC) pulmonary diseases that manifested as unilateral lung disease. MATERIALS AND METHODS. We retrospectively identified 674 patients with NB MAC pulmonary disease (PD) who underwent serial CT studies from January 2005 through December 2012. We selected patients with unilateral lung involvement as its initial manifestation. Retrospective analyses on serial CT findings in terms of presence and extent of lung abnormalities were performed. The organism identified (M. avium vs M. intracellulare) and treatment status were reviewed. To find the factors related to progression to involve both lungs, Cox regression analysis was performed. RESULTS. Unilateral MAC PD on initial CT was found in 47 patients (7%). Among them, 10 (21%) showed disease progression on follow-up CT to involve both lungs (mean evolving time, 1536 days). All 10 of these cases initially involved the right lung. Of these 10 patients, eight needed antibiotic treatment because of deteriorating imaging findings (4/8, 50%) or worsening symptoms (4/8, 50%). Initial total CT score (hazard ratio [HR], 1.414; 95% CI, 1.092-1.831; p < 0.01) and age (HR, 1.076; 95% CI, 1.004-1.154; p < 0.05) were related factors for disease progression in simple Cox regression test. CONCLUSION. Unilateral lung involvement of NB MAC PD is an occasional (7%) manifestation, and disease progressed in approximately 20% of patients in our study to involve both lungs. The imaging factor most related to disease progression appears to disease extent on initial CT.

Quantification of left ventricular trabeculae using cardiovascular magnetic resonance for the diagnosis of left ventricular non-compaction: evaluation of trabecular volume and refined semi-quantitative criteria.

BACKGROUND: Left ventricular non-compaction (LVNC) is an unclassified cardiomyopathy and there is no consensus on the diagnosis of LVNC. The aims of this study were to establish quantitative methods to diagnose LVNC using cardiovascular magnetic resonance (CMR) and to suggest refined semi-quantitative methods to diagnose LVNC. METHODS: This retrospective study included 145 subjects with mild to severe trabeculation of the left ventricle myocardium [24 patients with isolated LVNC, 33 patients with non-isolated LVNC, 30 patients with dilated cardiomyopathy (DCM) with non-compaction (DCMNC), 27 patients with DCM, and 31 healthy control subjects with mild trabeculation]. The left ventricular (LV) ejection fraction, global LV myocardial volume, trabeculated LV myocardial volume, and number of segments with late gadolinium enhancement were measured. In addition, the most prominent non-compacted (NC), compacted (C), normal mid-septum, normal mid-lateral wall, and apical trabeculation thicknesses on the end-diastolic frames of the long-axis slices were measured. RESULTS: In the patients with isolated LVNC, the percentage of trabeculated LV volume (TV%, â€‹42.6 ± 13.8 %) â€‹relative to total LV myocardial volume was 1.4 times higher than in those with DCM (30.3 ± 14.3 %, p < 0.001), and 1.7 times higher than in the controls (24.8 ± 7.1 %, p < 0.001). However, there was no significant difference in TV% between the isolated LVNC and DCMNC groups (47.1 ± 17.3 % in the DCMNC group; p = 0.210). The receiver operating characteristic curve analysis using Jenni's method for CMR classification as the standard diagnostic criteria revealed that a value of TV% above 34.6 % was predictive of NC with a specificity of 89.7 % (CI: 74.2 - 98.0 %) and a sensitivity of 66.1 % (CI: 52.6 - 77.9 %). A value of NC/septum over 1.27 was considered predictive for NC with a specificity of 82.8 % (CI: 64.2 - 94.2 %) and a sensitivity of 57.6 % (CI: 44.1 - 70.4 %). In addition, a value of apex/C above 3.15 was considered predictive of NC with a specificity of 93.1 % (CI: 77.2 - 99.2 %) and a sensitivity of 69.5 % (CI: 56.1 - 80.8 %). CONCLUSIONS: A trabeculated LV myocardial volume above 35 % of the total LV myocardial volume is diagnostic for LVNC with high specificity. Also, the apex/C and NC/septum ratios could be useful as supplementary diagnostic criteria.