Using Pretreatment Radiomics and Delta-Radiomics Features to Predict Non–Small Cell Lung Cancer Patient Outcomes

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions)

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics

OBJECTIVE

To investigate the relationship between CT radiomic features, pathological classification of pulmonary nodules, and evaluate the prediction effect of different stratified progressive radiomic models on the pathological classification of pulmonary nodules.

METHODS

Altogether, 189 patients pathologically confirmed with pulmonary nodules from July 2017 to August 2019 who had complete data were enrolled, including 71 patients with benign nodules, 51 with malignant non-invasive nodules, and 67 with invasive nodules. Three CT radiomic models were established respectively. Model 1 classified benign and malignant nodules (including malignant non-invasive and invasive nodules). Model 2 classified malignant non-invasive and invasive nodules. Model 3 classified benign, malignant non-invasive, and invasive nodules. High-throughput feature collection was carried out for all delineated regions of interest (ROIs), and the best models were established by screening features and classifiers using intelligent methods. ROC curves and areas under the curve (AUCs) were used to evaluate the prediction efficacy of the models by calculating the sensitivity, specificity, accuracies, positive predictive values, and negative predictive values.

RESULTS

Through Models 1, 2, and 3, we screened out 20, 2, and 20 radiomic features, respectively, and plotted the ROC curves. In the test group, the AUC values were 0.85, 0.89, and 0.84, respectively; the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 79.66 %, 70.42 %, 84.59 %, and 81.74 % and 67.57％ for Model 1, 88.06 %, 74.51 %, 82.2 %, 81.94 %, and 82.61 % for Model 2, and 71.34 %, 85.05 %, 70.37 %, 83.2 %, and 76.3 % for Model 3.

CONCLUSION

The radiomic feature models based on CT images could well reflect the differences between benign nodules, malignant non-invasive nodules, and invasive nodules, and assist in their classification.

Technical Note: ROdiomiX: A validated software for radiomics analysis of medical images in radiation oncology

PURPOSE

This study introduces an in-house-designed software platform (ROdiomiX) for the radiomics analysis of medical images in radiation oncology. ROdiomiX is a MATLAB-based framework for the computation of radiomic features and feature aggregation techniques in compliance with the Image-Biomarker-Standardization-Initiative (IBSI) guidelines, which includes preprocessing protocols and quantitative benchmark results for analysis of computational phantom images.

METHODS AND MATERIALS

The ROdiomiX software system consists of a series of computation cores implemented on the basis of the guidelines proposed by the IBSI. It is capable of quantitative computation of the following 11 different feature categories: Local-Intensity, Intensity-Histogram, Intensity-Based-Statistical, Intensity-Volume-Histogram, Gray-Level-Co-occurrence, Gray-Level-Run-Length, Gray-Level-Size-Zone, Gray-Level-Distance-Zone, Neighborhood-Grey-Tone-Difference, Neighboring-Grey-Level-Dependence, and Morphological feature. ROdiomiX was validated against benchmark values for the IBSI 3D digital phantom, as well as one designed in-house (HFH). The intraclass correlation coefficient (ICC) for estimating the degree of absolute agreement between ROdiomiX computation and benchmark values for different features at the 95% confidence level (CL) was used for comparison.

RESULTS

Among the 11 feature categories with 149 total features including 10 different feature aggregation methods (following the IBSI guidelines), the percent difference between absolute feature values computed by the ROdiomiX software and benchmark values reported for IBSI and HFH digital phantoms were 0.14% + 0.43% and 0.11% + 0.27%, respectively. The ICC values were >0.997 for all ten feature categories for both the IBSI and HFH digital phantoms.

CONCLUSION

The authors successfully developed a platform for computation of quantitative radiomic features. The image preprocessing and computational software cores were designed following the procedures specified by the IBSI. Benchmarking testing was in excellent agreement against the IBSI- and HFH-designed computational phantoms.

Radiomics Improves Cancer Screening and Early Detection

Imaging is a key technology in the early detection of cancers, including X-ray mammography, low-dose CT for lung cancer, or optical imaging for skin, esophageal, or colorectal cancers. Historically, imaging information in early detection schema was assessed qualitatively. However, the last decade has seen increased development of computerized tools that convert images into quantitative mineable data (radiomics), and their subsequent analyses with artificial intelligence (AI). These tools are improving diagnostic accuracy of early lesions to define risk and classify malignant/aggressive from benign/indolent disease. The first section of this review will briefly describe the various imaging modalities and their use as primary or secondary screens in an early detection pipeline. The second section will describe specific use cases to illustrate the breadth of imaging modalities as well as the benefits of quantitative image analytics. These will include optical (skin cancer), X-ray CT (pancreatic and lung cancer), X-ray mammography (breast cancer), multiparametric MRI (breast and prostate cancer), PET (pancreatic cancer), and ultrasound elastography (liver cancer). Finally, we will discuss the inexorable improvements in radiomics to build more robust classifier models and the significant limitations to this development, including access to well-annotated databases, and biological descriptors of the imaged feature data.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

INTRODUCTION

Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.

PATIENTS AND METHODS

In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.

RESULTS

The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.

CONCLUSIONS

These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9

Recently we showed that delta radiomics features (DRF) from daily CT-guided chemoradiation therapy (CRT) is associated with early prediction of treatment response for pancreatic cancer. CA19-9 is a widely used clinical biomarker for pancreatic cancer. The purpose of this work is to investigate if the predictive power of such biomarkers (DRF or CA19-9) can improve by combining both biomarkers. Daily non-contrast CTs acquired during routine CT-guided neoadjuvant CRT for 24 patients (672 datasets, in 28 daily fractions), along with their CA19-9, pathology reports and follow-up data were analyzed. The pancreatic head was segmented on each daily CT and radiomic features were extracted from the segmented regions. The time between the end of treatment and last follow-up was used to build a survival model. Patients were divided into two groups based on their pathological response. Spearman correlations were used to find the DRFs correlated to CA19-9. A regression model was built to examine the effect of combining CA19-9 and DRFs on response prediction. C-index was used to measure model effectiveness. The effect of a decrease in CA19-9 levels during treatment vs. failure of CA19-9 levels to normalize on survival was examined. Univariate- and multivariate Cox-regression analysis were performed to determine the effect of combining CA19-9 and DRFs on survival correlations. Spearman correlation showed that CA19-9 is correlated to DRFs (Entropy, cluster tendency and coarseness). An Increase in CA19-9 levels during treatment were correlated to a bad response, while a decline was correlated to a good response. Incorporating CA19-9 with DRFs increased the c-index from 0.57 to 0.87 indicating a stronger model. The univariate analysis showed that patients with decreasing CA19-9 had an improved median survival (68 months) compared to those with increasing levels (33 months). The 5-years survival was improved for the decreasing CA19-9 group (55%) compared to the increasing group (30%). The Cox-multivariate analysis showed that treatment related decrease in CA19-9 levels (p = 0.031) and DRFs (p = 0.001) were predictors of survival. The hazard-ratio was reduced from 0.73, p = 0.032 using CA19-9 only to 0.58, p = 0.028 combining DRFs with CA19-9. DRFs-CA19-9 combination has the potential to increasing the possibility for response-based treatment adaptation.

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening

Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of indeterminate pulmonary nodules. Radiomics is the process of extracting and analyzing image-based, quantitative features from a region-of-interest which then can be analyzed to develop decision support tools that can improve lung cancer screening. Although prior published research has shown that delta radiomics (i.e., changes in features over time) have utility in predicting treatment response, limited work has been conducted using delta radiomics in lung cancer screening. As such, we conducted analyses to assess the performance of incorporating delta with conventional (non delta) features using machine learning to predict lung nodule malignancy. We found the best improved area under the receiver operating characteristic curve (AUC) was 0.822 when delta features were combined with conventional features versus an AUC 0.773 for conventional features only. Overall, this study demonstrated the important utility of combining delta radiomics features with conventional radiomics features to improve performance of models in the lung cancer screening setting.