Rajagopalan, Srinivasan

Abstract Rationale In fungal endemic regions, differentiating benign from malignant pulmonary nodules on CT scans remains a major diagnostic challenge. Coccidioides nodules frequently resemble malignant lesions on CT and PET imaging, presenting significant challenges for differentiation, even among radiologists. Consequently, the benign biopsy and resection rate, especially in the Southwestern US, remains high. We previously developed [DOI:10.1371/journal.pone.0196910] and externally validated [DOI:10.1183/13993003.02485-2020] the Benign versus aggRessive nODule Evaluation using Radiomic Stratification (BRODERS) machine learning (ML) classifier to non-invasively distinguish between benign and malignant pulmonary nodules with an AUC of 0.87. However, its performance in certain benign nodules with malignant surface and texture characteristics like in coccidioidomycosis had a suboptimal AUC of 0.65. In this study, we redesigned our radiomics model by including numerous clinical features. Methods In this retrospective study, 113 pulmonary nodules (66 Coccidioides and 47 adenocarcinoma spectrum) detected on CTs with ground truth pathology diagnosis were identified by screening for key terms like “nodule”, “cocci”, and “adenocarcinoma” from both CT and pathology reports using a large language model trained on clinical textual data (Illuminate®). Clinical features such as demographics, smoking history, and laboratory studies were extracted by trained clinicians from the electronic medical record. All nodules were segmented and radiomic features extracted using a semi-automated seed growing algorithm previously described [DOI: 10.1097/JTO.0b013e3182843721]. The database was randomly split into an 80%-20% development and hold-out set prior to ML modeling. Feature selection for variables was conducted utilizing Elastic Net Regression, which provides a balanced approach between Lasso and Ridge regression. A logistic regression model was developed using the selected features and ground truth information of the training cohort. The true performance of the model was tested on the hold-out set. Results 160 radiomics and 22 clinical features were extracted. Elastic Net regression identified 44 features that contributed the most. Some of these clinical features include presence of satellite lesions, smoking history, and presence of positive IgG coccidiomycosis serology. The AUC of the ROC curve for the hold-out test set was 0.86. Conclusions The clinical features of significance that emerged during multimodal modeling are explainable. Designing explainable ML models is important, especially in the Southwestern US where Coccidioides nodules can appear to be malignant to both human experts and ML models. Although coccidioidomycosis serologies alone lack sensitivity and specificity to diagnose Coccidioides nodules, integrating them with a multimodal ML model can improve differentiation between benign Coccidioides and malignant adenocarcinoma nodules. This abstract is funded by: None

Introduction Bronchoscopic lung volume reduction with endobronchial valves (EBV) is an effective procedure for patient with severe emphysema to improve lung function, exercise tolerance, dyspnoea and quality of life. Optimisation of patient and treatment lobe selection is essential for successful EBV outcomes. While clinical selection criteria are rigorous, many centres use a multidisciplinary team and rely on previous clinical experience for the selection process. To aid objective clinical decision making, we present a mathematical model to facilitate patient and target lobe selection. Methods A multidisciplinary team reviewed quantitative high-resolution computed tomography (HRCT) analysis from 119 patients to select candidates for EBV and to select a treatment lobe. Two logistic regression models, (1) candidacy for EBV placement and (2) target-lobe selection, were developed based on the normalised distributions of the four quantitative HRCT variables (fissure completeness score, per cent of voxel density with HU < −910 and HU < −950, and lobar volumes) across all five lung lobes. An external cohort of 50 patients (25 candidates and 25 non-candidates) was used to validate the prediction model. Results Performance measures of the training cohort demonstrated an area under the curve (AUC) of 0.91, accuracy of 81%, sensitivity of 93% and specificity of 78% compared with the multidisciplinary teams’ target lobe selection. The validation cohort demonstrated an AUC of 0.89, accuracy of 84%, sensitivity of 88% and specificity of 83% compared with the multidisciplinary team decision making. Conclusions Endobronchial valve lung volume reduction remains a potent palliative measure to improve quality of life in patients with hyperinflated emphysema. Our model for target lobe selection harnesses the multidisciplinary experience at a tertiary care centre to objectively select candidates and target lobes to assist clinicians’ decision making. Future studies investigating prediction of lobar collapse and functional improvement after target lobe selection using our model are needed.

Abstract Rationale Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non–small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Objective Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. Methods This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Results Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83–0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Conclusions Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.

ABSTRACT Background and objective This study evaluated whether patients with combined pulmonary fibrosis and emphysema (CPFE) have an increased likelihood of pulmonary hypertension (PHT) when compared with idiopathic pulmonary fibrosis (IPF) patients without emphysema. Methods Two consecutive IPF populations having undergone transthoracic echocardiography were examined ( n = 223 and n = 162). Emphysema and interstitial lung disease (ILD) extent were quantified visually; ILD extent was also quantified by a software tool, CALIPER. Echocardiographic criteria categorized PHT risk. Results The prevalence of an increased PHT likelihood was 29% and 31% in each CPFE cohort. Survival at 12 months was 60% across both CPFE cohorts with no significantly worsened outcome identified when compared with IPF patients without emphysema. Using logistic regression models in both cohorts, total computed tomography (CT) disease extent (ILD and emphysema) predicted the likelihood of PHT. After adjustment for total disease extent, CPFE had no stronger association with PHT likelihood than IPF patients without emphysema. Conclusion Our findings indicate that the reported association between CPFE and PHT is explained by the summed baseline CT extents of ILD and emphysema. Once baseline severity is taken into account, CPFE is not selectively associated with a malignant microvascular phenotype, when compared with IPF patients without emphysema.

ABSTRACTBackground and objectiveTo determine whether computer‐based quantification (CALIPER software) is superior to visual computed tomography (CT) scoring in the identification of CT patterns indicative of restrictive and obstructive functional indices in hypersensitivity pneumonitis (HP).MethodsA total of 135 consecutive HP patients had CT parenchymal patterns evaluated quantitatively by both visual scoring and CALIPER. Results were evaluated against: forced vital capacity (FVC), total lung capacity (TLC), diffusing capacity for carbon monoxide (DLCO) and a composite physiological index (CPI) to identify which CT scoring method better correlated with functional indices.ResultsCALIPER‐derived scores of total interstitial lung disease extent correlated more strongly than visual scores: FVC (CALIPER R = 0.73, visual R = 0.51); DLCO (CALIPER R = 0.61, visual R = 0.48); and CPI (CALIPER R = 0·70, visual R = 0·55). The CT variable that correlated most strongly with restrictive functional indices was CALIPER pulmonary vessel volume (PVV): FVC R = 0.75, DLCO R = 0.68 and CPI R = 0.76.Ground‐glass opacity quantified by CALIPER alone demonstrated strong associations with restrictive functional indices: CALIPER FVC R = 0.65; DLCO R = 0.59; CPI R = 0.64; and visual = not significant. Decreased attenuation lung quantified by CALIPER was a better morphological measure of obstructive lung disease than equivalent visual scores as judged by relationships with TLC (CALIPER R = 0.63 and visual R = 0.12). All results were maintained on multivariate analysis.ConclusionCALIPER improved on visual scoring in HP as judged by restrictive and obstructive functional correlations. Decreased attenuation regions of the lung quantified by CALIPER demonstrated better linkages to obstructive lung physiology than visually quantified CT scores. A novel CALIPER variable, the PVV, demonstrated the strongest linkages with restrictive functional indices and could represent a new automated index of disease severity in HP.