Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers

TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk

Abstract Background There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case–control cohort. Methods Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. Results TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749–0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846–0.986) and this was significantly higher than that for either factor alone (P < 0.03). Conclusions These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen

18F-FSPG PET imaging for the evaluation of indeterminate pulmonary nodules.

Background18F-fluorodeoxyglucose (FDG) PET/CT is recommended for evaluation of intermediate-risk indeterminate pulmonary nodules (IPNs). While highly sensitive, the specificity of FDG remains suboptimal for differentiating malignant from benign nodules, particularly in areas where fungal lung diseases are prevalent. Thus, a cancer-specific imaging probe is greatly needed. In this study, we tested the hypothesis that a PET radiotracer (S)-4-(3-[18F]-fluoropropyl)-L-glutamic acid (FSPG) improves the diagnostic accuracy of IPNs compared to 18F-FDG PET/CT.MethodsThis study was conducted at a major academic medical center and an affiliated VA medical center. Twenty-six patients with newly discovered IPNs 7-30mm diameter or newly diagnosed lung cancer completed serial PET/CT scans utilizing 18F-FDG and 18F-FSPG, without intervening treatment of the lesion. The scans were independently reviewed by two dual-trained diagnostic radiology and nuclear medicine physicians. Characteristics evaluated included quantitative SUVmax values of the pulmonary nodules and metastases.ResultsA total of 17 out of 26 patients had cancer and 9 had benign lesions. 18F-FSPG was negative in 6 of 9 benign lesions compared to 7 of 9 with 18F-FDG. 18F-FSPG and 18F-FDG were positive in 14 of 17 and 12 of 17 malignant lesions, respectively. 18F-FSPG detected brain and intracardiac metastases missed by 18F-FDG PET in one case, while 18F-FDG detected a metastasis to the kidney missed by 18F-FSPG.ConclusionIn this pilot study, there was no significant difference in overall diagnostic accuracy between 18F-FSPG and 18F-FDG for the evaluation of IPNs and staging of lung cancer. Additional studies will be needed to determine the clinical utility of this tracer in the management of IPNs and lung cancer

Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas

<div><p>Lung adenocarcinoma (ADC), the most common lung cancer type, is recognized increasingly as a disease spectrum. To guide individualized patient care, a non-invasive means of distinguishing indolent from aggressive ADC subtypes is needed urgently. Computer-Aided Nodule Assessment and Risk Yield (CANARY) is a novel computed tomography (CT) tool that characterizes early ADCs by detecting nine distinct CT voxel classes, representing a spectrum of lepidic to invasive growth, within an ADC. CANARY characterization has been shown to correlate with ADC histology and patient outcomes. This study evaluated the inter-observer variability of CANARY analysis. Three novice observers segmented and analyzed independently 95 biopsy-confirmed lung ADCs from Vanderbilt University Medical Center/Nashville Veterans Administration Tennessee Valley Healthcare system (VUMC/TVHS) and the Mayo Clinic (Mayo). Inter-observer variability was measured using intra-class correlation coefficient (ICC). The average ICC for all CANARY classes was 0.828 (95% CI 0.76, 0.895) for the VUMC/TVHS cohort, and 0.852 (95% CI 0.804, 0.901) for the Mayo cohort. The most invasive voxel classes had the highest ICC values. To determine whether nodule size influenced inter-observer variability, an additional cohort of 49 sub-centimeter nodules from Mayo were also segmented by three observers, with similar ICC results. Our study demonstrates that CANARY ADC classification between novice CANARY users has an acceptably low degree of variability, and supports the further development of CANARY for clinical application.</p></div

Tumoral Densities of T-Cells and Mast Cells Are Associated With Recurrence in Early-Stage Lung Adenocarcinoma

Introduction: Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD. Methods: This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement. Results: Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27–0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15–0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing. Conclusions: Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set

Intra-class correlation coefficient (ICC) is highest amongst the CANARY classes representing most invasive ADC features.

<p>Intra-class correlation coefficient (ICC), a measure of agreement of quantitative assessments made by different observers evaluating the same quantity, was calculated for each CANARY class, the VIRO group, and the average (Avg.) of all classes. An ICC of 0.8–1 reflects high agreement between users. These calculations were performed for the VUMC/TVHS and the Mayo cohorts, as well as for each subgroup of patients scanned by a particular CT scanner (GE Medical Systems, Philips, and Siemens). ICCs and 95% CI for each voxel class are shown below. The number of patients within each cohort is listed in parentheses.</p