The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

/pdf/deep-learning-with-radiomics-for-disease-diagnosis-and-dkvvhcd5.pdf

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Artificial Intelligence-based Prediction of Clinical Outcome in Immunotherapy and Targeted Therapy of Lung Cancer.

Abstract Radiomics is a technique that extracts quantitative features from medical images using data‐characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.13869

Feature selection methods and predictive models in CT lung cancer radiomics

Background Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression. Methods We analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score's patient-stratification ability. Results The prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017). Conclusions Radiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient's individual risk profile.

/pdf/mri-radiomic-signature-predicts-intracranial-progression-gga95425lj.pdf

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer

Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.

/pdf/impact-of-feature-selection-methods-and-subgroup-factors-on-45ydypo54f.pdf

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.

BACKGROUND Here, we aimed to assess the association of ALK variants and alterations with ensartinib response duration in NSCLC, and explore the potential value of computed tomography (CT) radiomic features in predicting progression-free survival (PFS). METHODS We enrolled 88 patients with identified ALK variant NSCLC in a multicenter phase 2 trial, and assessed the impact of ALK variants and secondary ALK alterations on the clinical outcome (response duration) of patients receiving ensartinib. We also established a multifactorial model of clinicopathological and quantitative CT radiomic features to predict PFS and risk stratification. Kaplan-Meier analysis was conducted to identify risk factors for tumor progression. RESULTS Univariate analysis indicated a statistical difference (p = 0.035) in PFS among ALK variants in three classifications (V1, V3, and other variants). Secondary ALK alterations were adversely associated with PFS both in univariate (p = 0.008) and multivariate (p = 0.04) analyses and could identify patients at high risk for early progression in the Kaplan-Meier analysis (p = 0.002). Additionally, response duration to crizotinib <1 year and liver metastasis were adversely associated with PFS. The combined model, composed of clinicopathological signature and CT radiomic signature, showed good prediction ability with the area under the receiver operating characteristic curve being 0.85, and 0.89 in the training and validation dataset respectively. CONCLUSIONS Our study showed that secondary ALK alterations were adversely associated with ensartinib efficacy, and that ALK variants might not correlate with PFS. The quantitative radiomic signature provided added prognostic prediction value to the clinicopathological features.

Association of anaplastic lymphoma kinase variants and alterations with ensartinib response duration in non-small cell lung cancer.

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MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer

Association of anaplastic lymphoma kinase variants and alterations with ensartinib response duration in non-small cell lung cancer.