Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: radiologic biopsy, risk stratification and future directions

Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA

While CT lung cancer screening reduces lung cancer-specific mortality, there are remaining challenges. Radiomic tools promiss to address these challenges, however, they are subject to interobserver variability if semi-automated segmentation techniques are used. Herein we report interobserver variability for two validated radiomic tools, BRODERS (Benign versus aggRessive nODule Evaluation using Radiomic Stratification) and CANARY (Computer-Aided Nodule Assessment and Risk Yield). We retrospectively analyzed the CT images of 95 malignant lung nodules of the adenocarcinoma spectrum using BRODERS and CANARY. Cases were identified at Mayo Clinic (n = 45) and Vanderbilt University Medical Center and Nashville/Veteran Administration Tennessee Valley Health Care System (n = 50). Three observers with different training levels (medical student, internal medicine resident and thoracic radiology fellow) each performed lung nodule segmentation. All methods were carried out in accordance with relevant guidelines and regulations. Interclass correlation coefficients (ICC) of 0.77, 0.98 and 0.97 for the average nodule volume, BRODERS cancer probability and Score Indicative of Lesion Aggression (SILA) which summarizes the distribution of the CANARY exemplars indicated good to excellent reliability, respectively. The dice similarity coefficient was 0.79 and 0.81 for the data sets from the two institutions. BRODERS and CANARY are robust radiomics tools with excellent interobserver variability. These tools are simple and reliable regardless the observer/operator's level of training.

Cancer-associated fibroblasts in early-stage lung adenocarcinoma correlate with tumor aggressiveness

Lung adenocarcinoma (LUAD) is the predominant type of lung cancer in the U.S. and exhibits a broad variety of behaviors ranging from indolent to aggressive. Identification of the biological determinants of LUAD behavior at early stages can improve existing diagnostic and treatment strategies. Extracellular matrix (ECM) remodeling and cancer-associated fibroblasts play a crucial role in the regulation of cancer aggressiveness and there is a growing need to investigate their role in the determination of LUAD behavior at early stages. We analyzed tissue samples isolated from patients with LUAD at early stages and used imaging-based biomarkers to predict LUAD behavior. Single-cell RNA sequencing and histological assessment showed that aggressive LUADs are characterized by a decreased number of ADH1B+ CAFs in comparison to indolent tumors. ADH1B+ CAF enrichment is associated with distinct ECM and immune cell signatures in early-stage LUADs. Also, we found a positive correlation between the gene expression of ADH1B+ CAF markers in early-stage LUADs and better survival. We performed TCGA dataset analysis to validate our findings. Identified associations can be used for the development of the predictive model of LUAD aggressiveness and novel therapeutic approaches.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

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.

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Purpose

Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT).

Approach

In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort (n = 1189 ) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence.

Results

Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (p < 0.01 ). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCO m 2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (χ 2 = 7.48 , p = 0.02 ).

Conclusions

We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.

Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers

BACKGROUND: Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes. METHODS: Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes. RESULTS: Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS). CONCLUSION: We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.

The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines

PURPOSE

To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum.

METHOD

An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention.

RESULTS

The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans <0.56% group (n = 13) could be obviated resulting in an overall reduction of 18.6% (45/242) CT scans in the benign cohort. In the 80% group (n = 10), expedited intervention of malignant nodules could result in a 3.6-month reduction in time delay in 5 cancer patients.

CONCLUSION

We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.

Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

The National Lung Screening Trial (NLST) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDCT screening is overdiagnosis of slow growing and indolent cancers. In this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. Incident lung cancer patients from the NLST were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression Tree to stratify patients into three risk-groups based on two radiomics features (NGTDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. Our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. These patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes.