Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer

Background

Immunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians.

Methods

Computed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity.

Results

A cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%).

Conclusions

Whole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.

Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed

BACKGROUND

To investigate the value of preoperative computed tomography (CT) texture features, routine imaging features, and clinical features in the prognosis of non-small cell lung cancer (NSCLC) after radical resection.

METHODS

Demographic parameters and clinically features were analyzed in 107 patients with stage I-IIIB NSCLC, while 73 of these patients received CT scanning and radiomic characteristics for prognosis assessment. Texture analysis features include histogram, gray size area matrix and gray co-occurrence matrix features. The clinical risk features were identified using univariate and multivariate logistic analyses. By incorporating the radiomics score (Rad-score) and clinical risk features with multivariate cox regression, a combined nomogram was built. The nomogram performance was assessed by its calibration, clinical usefulness and Harrell's concordance index (C-index). The 5-year OS between the dichotomized subgroups was compared using Kaplan-Meier (KM) analysis and the log-rank test.

RESULTS

Consisting of 4 selected features, the radiomics signature showed a favorable discriminative performance for prognosis, with an AUC of 0.91 (95% CI: 0.84 ~ 0.97). The nomogram, consisting of the radiomics signature, N stage, and tumor size, showed good calibration. The nomogram also exhibited prognostic ability with a C-index of 0.91 (95% CI, 0.86-0.95) for OS. The decision curve analysis indicated that the nomogram was clinically useful. According to the KM survival curves, the low-risk group had higher 5-year survival rate compared to high-risk.

CONCLUSION

The as developed nomogram, combining with preoperative radiomics evidence, N stage, and tumor size, has potential to preoperatively predict the prognosis of NSCLC with a high accuracy and could assist to treatment for the NSCLC patients in the clinic.

Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians

Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.

Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review

AIM

Head and neck squamous cell cancer (HNSCC) is the ninth most common tumor worldwide. Neck lymph node (LN) status is the major indicator of prognosis in all head and neck cancers, and the early detection of LN involvement is crucial in terms of therapy and prognosis. Diffusion-weighted imaging (DWI) is a non- invasive imaging technique used in magnetic resonance imaging (MRI) to characterize tissues based on the displacement motion of water molecules. This review aims to provide an overview of the current literature concerning quantitative diffusion imaging for LN staging in patients with HNSCC.

METHODS

This systematic review performed a literature search on the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) for all relevant, peer-reviewed literature on the subject following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria, using the keywords: DWI, MRI, head and neck, staging, lymph node.

RESULTS

After excluding reviews, meta-analyses, case reports, and bibliometric studies, 18 relevant papers out of the 567 retrieved were selected for analysis.

CONCLUSIONS

DWI improves the diagnosis, treatment planning, treatment response evaluation, and overall management of patients affected by HNSCC. More robust data to clarify the role of apparent diffusion coefficient (ADC) and DWI parameters are needed to develop models for prognosis and prediction in HNSCC cancer using MRI.

Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6−8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan−Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.

Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy

Background

This study aims to establish and validate a predictive model based on radiomics features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT).

Methods

We retrospectively reviewed 131 patients. Patients were randomly divided into the training (n = 105) and validation (n = 26) cohorts. The clinical target volume was contoured on pre-RT computed tomography images and 48 textural features were extracted. The least absolute shrinkage and selection operator regression was used to determine the radiomics score (rad-score). A nomogram based on rad-score, clinical features, and dosimetric parameters was developed using the results of multivariate regression analysis. The predictive nomogram was evaluated using Harrell’s concordance index (C-index), area under the curve (AUC), and calibration curve.

Results

Two radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67–0.79) and an AUC of 0.71 (95% CI, 0.62–0.79). The predictive accuracy was assessed by a calibration curve.

Conclusion

The radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.

Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors

Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors. Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis. Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome. Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.

Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery

Simple Summary

The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery.

Abstract

We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

Simple Summary

The introduction of immune checkpoint inhibitors has represented a milestone in cancer treatment. Despite PD-L1 expression being the standard biomarker used before the start of therapy, there is still a strict need to identify complementary non-invasive biomarkers in order to better select patients. In this context, radiomics is an emerging approach for examining medical images and clinical data by capturing multiple features hidden from human eye and is potentially able to predict response assessment and survival in the course of immunotherapy. We reviewed the available studies investigating the role of radiomics in cancer patients, focusing on non-small cell lung cancer treated with immune checkpoint inhibitors. Although preliminary research shows encouraging results, different issues need to be solved before radiomics can enter into clinical practice.

Abstract

Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an “ideal” biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.

Lung Cancer Imaging: Screening Result and Nodule Management

Background: Lung cancer (LC) represents the main cause of cancer-related deaths worldwide, especially because the majority of patients present with an advanced stage of the disease at the time of diagnosis. This systematic review describes the evidence behind screening results and the current guidelines available to manage lung nodules. Methods: This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The following electronic databases were searched: PubMed, EMBASE, and the Web of Science. Results: Five studies were included in the systematic review. The study cohort included 46,364 patients, and, in this case series, LC was detected in 9028 patients. Among the patients with detected LC, 1261 died of lung cancer, 3153 died of other types of cancers and 4614 died of other causes. Conclusions: This systematic review validates the use of CT in LC screening follow-ups, and bids for future integration and implementation of nodule management protocols to improve LC screening, avoid missed cancers and to reduce the number of unnecessary investigations.

Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies

Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients’ CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients’ CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels.

Distinctive Role of the Systemic Inflammatory Profile in Non-Small-Cell Lung Cancer Younger and Elderly Patients Treated with a PD-1 Immune Checkpoint Blockade: A Real-World Retrospective Multi-Institutional Analysis

An immune checkpoint blockade with mAbs to PD-1 and PD-L1 is an expanding therapeutic option for mNSCLC patients. This treatment strategy is based on the use of mAbs able to restore the anti-tumor activity of intratumoral T cells inhibited by PD-1 binding to PD-L1/2 on tumor and inflammatory cells. It has been speculated that a chronic status of systemic inflammation as well as the immunosenescence physiologically occurring in elderly patients may affect the efficacy of the treatment and the occurrence of irAEs. We performed a multi-institutional retrospective study aimed at evaluating the effects of these mAbs (nivolumab or atezolizumab) in 117 mNSCLC patients younger (90 cases) and older (27 cases) than 75 years in correlation with multiple inflammatory parameters (NLR, CRP, ESR, LDH and PCT). No differences were observed when the cohorts were compared in terms of the frequency of PFS, OS, inflammatory markers and immune-related adverse events (irAEs). Similarly, the occurrence of irAEs was strictly correlated with a prolonged OS survival in both groups. On the contrary, a negative correlation between the high baseline levels of inflammatory markers and OS could be demonstrated in the younger cohort only. Overall, PD-1/PD-L1-blocking mAbs were equally effective in young and elderly mNSCLC patients; however, the detrimental influence of a systemic inflammation at the baseline was only observed in young patients, suggesting different aging-related inflammation immunoregulative effects.

Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

Simple Summary

Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient’s condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy.

Abstract

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.

A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer

OBJECTIVE

The status of lymph node metastasis (LNM) is highly correlated with the recurrence and survival outcomes of patients with lung cancer. Thus, a tool that predicts LNM could benefit patient treatment and prognosis. The present study established a new radiomic model by combining computed tomography (CT) radiomic features and clinical parameters to predict the LNM status in patients with non-small cell lung cancer (NSCLC).

METHODS

Demographic parameters and clinical laboratory values were analyzed in 217 patients with stage I-IIIB NSCLC; 107 of the patients received CT scanning and radiomic characteristics were used for LNM assessment (76 in the training cohort and 31 in the validation cohort). The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression model were used to select the most predictive features on the basis of the 76 patients in the training set. The value of the area under the receiver operator characteristic (ROC) curve (AUC) was adopted to determine the correlation between LN status and the radiomics signature in training cohorts and then validated in the 31 patients of validation set. The radiomics nomogram was analyzed using univariate and multivariate logistic regression. Decision curve analysis (DCA) was performed to evaluate the clinical utility of this model.

RESULTS

This was a retrospective study. Five radiomic characteristics were significantly correlated with LNM in the two cohorts (P < 0.05). The radiomic nomogram that incorporated the above radiomic characteristics, the RDW, and the CT-based LN status had satisfactory discrimination and calibration in the training (AUC, 0.79; 95% CI 0.69-0.89) and validation cohorts (AUC, 0.70; 95% CI 0.50-0.89).The DCA showed that the developed nomogram had promising clinical utility.

CONCLUSIONS

The developed nomogram, combined with preoperative radiomics evidence, the RDW, and the CT-based LN status, has the potential to preoperatively predict LNM with high accuracy and can facilitate the prediction of LN status for NSCLC patients.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage

OBJECTIVE

To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.

METHODS

While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer.

RESULTS

The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods.

CONCLUSION

The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis

PURPOSE

Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC.

METHODS

We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed.

RESULTS

Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001).

CONCLUSIONS

Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

Objectives

The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.

Methods

In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.

Results

The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.

Conclusions

Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Simple Summary

This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy.

Abstract

Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Hypoxia in Lung Cancer Management: A Translational Approach

Simple Summary

Hypoxia is a common feature of lung cancers. Nonetheless, no guidelines have been established to integrate hypoxia-associated biomarkers in patient management. Here, we discuss the current knowledge and provide translational novel considerations regarding its clinical detection and targeting to improve the outcome of patients with non-small-cell lung carcinoma of all stages.

Abstract

Lung cancer represents the first cause of death by cancer worldwide and remains a challenging public health issue. Hypoxia, as a relevant biomarker, has raised high expectations for clinical practice. Here, we review clinical and pathological features related to hypoxic lung tumours. Secondly, we expound on the main current techniques to evaluate hypoxic status in NSCLC focusing on positive emission tomography. We present existing alternative experimental approaches such as the examination of circulating markers and highlight the interest in non-invasive markers. Finally, we evaluate the relevance of investigating hypoxia in lung cancer management as a companion biomarker at various lung cancer stages. Hypoxia could support the identification of patients with higher risks of NSCLC. Moreover, the presence of hypoxia in treated tumours could help clinicians predict a worse prognosis for patients with resected NSCLC and may help identify patients who would benefit potentially from adjuvant therapies. Globally, the large quantity of translational data incites experimental and clinical studies to implement the characterisation of hypoxia in clinical NSCLC management.

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Simple Summary

This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine.

Abstract

Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.

Radiomic biomarkers of tumor immune biology and immunotherapy response

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

CT based radiomic approach on first line pembrolizumab in lung cancer

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.

Radiomics in immuno-oncology

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer

Purpose

This study aims to develop a CT-based radiomics model to predict clinical outcomes of advanced non-small-cell lung cancer (NSCLC) patients treated with nivolumab.

Methods

Forty-six stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled. After segmenting primary tumors depicting on the pre-anti-PD1 treatment CT images, 1,106 radiomics features were computed and extracted to decode the imaging phenotypes of these tumors. A L1-based feature selection method was applied to remove the redundant features and build an optimal feature pool. To predict the risk of progression-free survival (PFS) and overall survival (OS), the selected image features were used to train and test three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier, and Gaussian Naïve Bayes classifier. Finally, the overall patients were stratified into high and low risk subgroups by using prediction scores obtained from three classifiers, and Kaplan–Meier survival analysis was conduct to evaluate the prognostic values of these patients.

Results

To predict the risk of PFS and OS, the average area under a receiver operating characteristic curve (AUC) value of three classifiers were 0.73 ± 0.07 and 0.61 ± 0.08, respectively; the corresponding average Harrell’s concordance indexes for three classifiers were 0.92 and 0.79. The average hazard ratios (HR) of three models for predicting PFS and OS were 6.22 and 3.54, which suggested the significant difference of the two subgroup’s PFS and OS (p<0.05).

Conclusion

The pre-treatment CT-based radiomics model provided a promising way to predict clinical outcomes for advanced NSCLC patients treated with nivolumab.

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades

Purpose

To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.

Materials and Methods

A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.

Result

Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.

Conclusion

In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

Pitfalls and differential diagnosis on adrenal lesions: current concepts in CT/MR imaging: a narrative review

The purpose of this pictorial essay is to review the imaging findings of adrenal lesions. Adrenal lesions could be divided into functioning or non-functioning masses, primary or metastatic, and benign or malignant. Imaging techniques have undergone significant advances in recent years. The most significant objective of adrenal imaging is represented by the detection and, when possible, characterization of adrenal lesions in order to direct patient management correctly. The detection and management of adrenal lesions is based on cross-sectional imaging obtained with non-contrast CT (tumour density), contrast-enhanced CT including delayed washout (either absolute percentage washout or relative percentage one) and finally with MR chemical shift analysis (loss of signal intensity between in-phase and out-of-phase images including both qualitative and quantitative estimates of signal loss). The small incidental adrenal nodules are benign, in most of cases; some tumors such as lipid-rich adenoma and myelolipoma have characteristic features that can be diagnosed accurately in CT. On contrary, if the presenting contrast-enhanced CT shows an adrenal mass with uncertain or malignant morphologic features, particularly in patients with a known history of malignancy, further evaluations should be considered. The most significative implications for radiologists are represented by how to assess risk of malignancy on imaging and what follow-up to indicate if an adrenal incidentaloma is not surgically removed.

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia

Purpose

To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables.

Materials and Methods

This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed.

Results

Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission.

Conclusion

Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.

Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

Novel Nuclear Medicine Imaging Applications in Immuno-Oncology

The global immuno-oncology pipeline has grown progressively in recent years, leading cancer immunotherapy to become one of the main issues of the healthcare industry. Despite their success in the treatment of several malignancies, immune checkpoint inhibitors (ICIs) perform poorly in others. Again, ICIs action depends on such a multitude of clinico-pathological features, that the attempt to predict responders/long-responders with ad-hoc built immunograms revealed to be quite complex. In this landscape, the role of nuclear medicine might be crucial, with first interesting evidences coming from small case series and pre-clinical studies. Positron-emission tomography (PET) techniques provide functional information having a predictive and/or prognostic value in patients treated with ICIs or adoptive T-cell therapy. Recently, a characterization of the tumor immune microenvironment (TiME) pattern itself has been shown to be feasible through the use of different radioactive tracers or image algorithms, thus adding knowledge about tumor heterogeneity. Finally, nuclear medicine exams permit an early detection of immune-related adverse events (irAEs), with on-going clinical trials investigating their correlation with patients’ outcome. This review depicts the recent advances in molecular imaging both in terms of non-invasive diagnosis of TiME properties and benefit prediction from immunotherapeutic agents.

Artificial intelligence to codify lung CT in Covid-19 patients

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already assumed pandemic proportions, affecting over 100 countries in few weeks. A global response is needed to prepare health systems worldwide. Covid-19 can be diagnosed both on chest X-ray and on computed tomography (CT). Asymptomatic patients may also have lung lesions on imaging. CT investigation in patients with suspicion Covid-19 pneumonia involves the use of the high-resolution technique (HRCT). Artificial intelligence (AI) software has been employed to facilitate CT diagnosis. AI software must be useful categorizing the disease into different severities, integrating the structured report, prepared according to subjective considerations, with quantitative, objective assessments of the extent of the lesions. In this communication, we present an example of a good tool for the radiologist (Thoracic VCAR software, GE Healthcare, Italy) in Covid-19 diagnosis (Pan et al. in Radiology, 2020. https://doi.org/10.1148/radiol.2020200370). Thoracic VCAR offers quantitative measurements of the lung involvement. Thoracic VCAR can generate a clear, fast and concise report that communicates vital medical information to referring physicians. In the post-processing phase, software, thanks to the help of a colorimetric map, recognizes the ground glass and differentiates it from consolidation and quantifies them as a percentage with respect to the healthy parenchyma. AI software therefore allows to accurately calculate the volume of each of these areas. Therefore, keeping in mind that CT has high diagnostic sensitivity in identifying lesions, but not specific for Covid-19 and similar to other infectious viral diseases, it is mandatory to have an AI software that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.