Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

CALGB 80802 (Alliance): Impact of Sorafenib with and without Doxorubicin on Hepatitis C Infection in Patients with Advanced Hepatocellular Carcinoma.

Sorafenib blocks nonstructural protein 5A (NS5A)-recruited c-Raf-mediated hepatitis C virus (HCV) replication and gene expression. Release of Raf-1-Ask-1 dimer and inhibition of Raf-1 via sorafenib putatively differ in the presence or absence of doxorubicin. Cancer and Leukemia Group B (CALGB) 80802 (Alliance) randomized phase III trial of doxorubicin plus sorafenib versus sorafenib in patients with advanced hepatocellular carcinoma (HCC), showed no improvement in median overall survival (OS). Whether HCV viral load impacts therapy and whether any correlation between HCV titers and outcome based on HCV was studied. In patients with HCV, HCV titer levels were evaluated at baseline and at multiple postbaseline timepoints until disease progression or treatment discontinuation. HCV titer levels were evaluated in relation to OS and progression-free survival (PFS). Among 53 patients with baseline HCV data, 12 patients had undetectable HCV (HCV-UN). Postbaseline HCV titer levels did not significantly differ between treatment arms. One patient in each arm went from detectable to HCV-UN with greater than 2 log-fold titer levels reduction. Aside from these 2 HCV-UN patients, HCV titers remained stable on treatment. Patients who had HCV-UN at baseline were 3.5 times more likely to progress and/or die from HCC compared with HCV detectable (HR = 3.51; 95% confidence interval: 1.58-7.78; P = 0.002). HCV titer levels remained unchanged, negating any sorafenib impact onto HCV titer levels. Although an overall negative phase III study, patients treated with doxorubicin plus sorafenib and sorafenib only, on CALGB 80802 had worse PFS if HCV-UN. Higher levels of HCV titers at baseline were associated with significantly improved PFS. SIGNIFICANCE: Sorafenib therapy for HCC may impact HCV replication and viral gene expression. In HCV-positive patients accrued to CLAGB 80802 phase III study evaluating the addition of doxorubicin to sorafenib, HCV titer levels were evaluated at baseline and different timepoints. Sorafenib did not impact HCV titer levels. Despite an improved PFS in patients with detectable higher level HCV titers at baseline, no difference in OS was noted.

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. METHODS: We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. RESULTS: Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. CONCLUSION: Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. CLINICAL RELEVANCE STATEMENT: This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. KEY POINTS: • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Three-Dimensional Margin as a Predictor of Local Tumor Progression after Microwave Ablation: Intraprocedural versus 4-8-Week Postablation Assessment.

PURPOSE: To evaluate the prognostic accuracy of intraprocedural and 4-8-week (current standard) post-microwave ablation zone (AZ) and margin assessments for prediction of local tumor progression (LTP) using 3-dimensional (3D) software. MATERIALS AND METHODS: Data regarding 100 colorectal liver metastases (CLMs) in 75 patients were collected from 2 prospective fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT)-guided microwave ablation (MWA) trials. The target CLMs and theoretical 5- and 10-mm margins were segmented and registered intraprocedurally and at 4-8 weeks after MWA contrast-enhanced CT (or magnetic resonance [MR] imaging) using the same methodology and 3D software. Tumor and 5- and 10-mm minimal margin (MM) volumes not covered by the AZ were defined as volumes of insufficient coverage (VICs). The intraprocedural and 4-8-week post-MWA VICs were compared as predictors of LTP using receiver operating characteristic curve analysis. RESULTS: The median follow-up time was 19.6 months (interquartile range, 7.97-36.5 months). VICs for 5- and 10-mm MMs were predictive of LTP at both time assessments. The highest accuracy for the prediction of LTP was documented with the intra-ablation 5-mm VIC (area under the curve [AUC], 0.78; 95% confidence interval, 0.66-0.89). LTP for a VIC of 6-10-mm margin category was 11.4% compared with 4.3% for >10-mm margin category (P < .001). CONCLUSIONS: A 3D 5-mm MM is a critical endpoint of thermal ablation, whereas optimal local tumor control is noted with a 10-mm MM. Higher AUCs for prediction of LTP were achieved for intraprocedural evaluation than for the 4-8-week postablation 3D evaluation of the AZ.

Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma.

Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.

Integrating [18F]-Fluorodeoxyglucose Positron Emission Tomography with Computed Tomography with Radiation Therapy and Immunomodulation in Precision Therapy for Solid Tumors.

[18F]-FDG positron emission tomography with computed tomography (PET/CT) imaging is widely used to enhance the quality of care in patients diagnosed with cancer. Furthermore, it holds the potential to offer insight into the synergic effect of combining radiation therapy (RT) with immuno-oncological (IO) agents. This is achieved by evaluating treatment responses both at the RT and distant tumor sites, thereby encompassing the phenomenon known as the abscopal effect. In this context, PET/CT can play an important role in establishing timelines for RT/IO administration and monitoring responses, including novel patterns such as hyperprogression, oligoprogression, and pseudoprogression, as well as immune-related adverse events. In this commentary, we explore the incremental value of PET/CT to enhance the combination of RT with IO in precision therapy for solid tumors, by offering supplementary insights to recently released joint guidelines.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Modeling Tumor Growth Using Partly Conditional Survival Models: A Case Study in Colorectal Cancer.

PURPOSE: There are multiple approaches to modeling the relationship between longitudinal tumor measurements obtained from serial imaging and overall survival. Many require strong assumptions that are untestable and debatable. We illustrate how to apply a novel, more flexible approach, the partly conditional (PC) survival model, using images acquired during a phase III, randomized clinical trial in colorectal cancer as an example. METHODS: PC survival approaches were used to model longitudinal volumetric computed tomography data of 1,025 patients in the completed VELOUR trial, which evaluated adding aflibercept to infusional fluorouracil, leucovorin, and irinotecan for treating metastatic colorectal cancer. PC survival modeling is a semiparametric approach to estimating associations of longitudinal measurements with time-to-event outcomes. Overall survival was our outcome. Covariates included baseline tumor burden, change in tumor burden from baseline to each follow-up time, and treatment. Both unstratified and time-stratified models were investigated. RESULTS: Without making assumptions about the distribution of the tumor growth process, we characterized associations between the change in tumor burden and survival. This change was significantly associated with survival (hazard ratio [HR], 1.04; 95% CI, 1.02 to 1.05; P < .001), suggesting that aflibercept works at least in part by altering the tumor growth trajectory. We also found baseline tumor size prognostic for survival even when accounting for the change in tumor burden over time (HR, 1.02; 95% CI, 1.01 to 1.02; P < .001). CONCLUSION: The PC modeling approach offers flexible characterization of associations between longitudinal covariates, such as serially assessed tumor burden, and survival time. It can be applied to a variety of data of this nature and used as clinical trials are ongoing to incorporate new disease assessment information as it is accumulated, as indicated by an example from colorectal cancer.

Immunotherapy in the Treatment of Localized Genitourinary Cancers.

Importance: A true revolution in the management of advanced genitourinary cancers has occurred with the discovery and adoption of immunotherapy (IO). The therapeutic benefits of IO were recently observed not to be solely confined to patients with disseminated disease but also in select patients with localized and locally advanced genitourinary neoplasms. Observations: KEYNOTE-057 demonstrated the benefit of pembrolizumab monotherapy for treating high-risk nonmuscle invasive bladder cancer unresponsive to bacillus Calmette-Guérin (BCG), resulting in recent US Food and Drug Administration approval. Furthermore, a current phase 3 trial (Checkmate274) demonstrated a disease-free survival benefit with the administration of adjuvant nivolumab vs placebo in muscle-invasive urothelial carcinoma after radical cystectomy. In addition, the recent highly publicized phase 3 KEYNOTE 564 trial demonstrated a recurrence-free survival benefit of adjuvant pembrolizumab in patients with high-risk localized/locally advanced kidney cancer. Conclusions and Relevance: The adoption and integration of IO in the management of localized genitourinary cancers exhibiting aggressive phenotypes are becoming an emerging therapeutic paradigm. Clinical oncologists and scientists should become familiar with these trials and indications because they are likely to dramatically change our treatment strategies in the months and years to come.

Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials.

BACKGROUND: Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies. PURPOSE: We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy. METHODS: We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity. RESULTS: In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023). CONCLUSION: This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment. CLINICAL RELEVANCE STATEMENT: Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health. KEY POINTS: •Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).

Is local review of positron emission tomography scans sufficient in diffuse large B-cell lymphoma clinical trials? A CALGB 50303 analysis.

BACKGROUND: Quantitative methods of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) interpretation, including the percent change in FDG uptake from baseline (ΔSUV), are under investigation in lymphoma to overcome challenges associated with visual scoring systems (VSS) such as the Deauville 5-point scale (5-PS). METHODS: In CALGB 50303, patients with DLBCL received frontline R-CHOP or DA-EPOCH-R, and although there were no significant associations between interim PET responses assessed centrally after cycle 2 (iPET) using 5-PS with progression-free survival (PFS) or overall survival (OS), there were significant associations between central determinations of iPET ∆SUV with PFS/OS. In this patient cohort, we retrospectively compared local vs central iPET readings and evaluated associations between local imaging data and survival outcomes. RESULTS: Agreement between local and central review was moderate (kappa = 0.53) for VSS and high (kappa = 0.81) for ∆SUV categories (<66% vs. ≥66%). ∆SUV ≥66% at iPET was significantly associated with PFS (p = 0.03) and OS (p = 0.002), but VSS was not. Associations with PFS/OS when applying local review vs central review were comparable. CONCLUSIONS: These data suggest that local PET interpretation for response determination may be acceptable in clinical trials. Our findings also highlight limitations of VSS and call for incorporation of more objective measures of response assessment in clinical trials.

Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC.

INTRODUCTION: We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed tomography (CT) images obtained from patients with NSCLC treated with nivolumab or chemotherapy. METHODS: The radiomic signature was developed in patients with NSCLC treated with nivolumab in CheckMate-017, -026, and -063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semiautomatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomic signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies. RESULTS: A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in the mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy. CONCLUSIONS: The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

Emerging and Evolving Concepts in Cancer Immunotherapy Imaging.

Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.

A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [18F]FDG PET/CT: a pathology-controlled study.

OBJECTIVES: Initial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [18F]FDG PET/CT provides insufficient and variable diagnostic performance. We aimed to develop and validate a machine-learning-based combination of criteria on [18F]FDG PET/CT to accurately identify pelvic LN involvement in bladder cancer patients. METHODS: Consecutive patients with localized MIBC who performed preoperative [18F]FDG PET/CT between 2010 and 2017 were retrospectively assigned to training (n = 129) and validation (n = 44) sets. The reference standard was the pathological status after extended pelvic LN dissection. In the training set, a random forest algorithm identified the combination of criteria that best predicted LN status. The diagnostic performances (AUC) and interrater agreement of this combination of criteria were compared to a consensus of experts. RESULTS: The overall prevalence of pelvic LN involvement was 24% (n = 41/173). In the training set, the top 3 features were derived from pelvic LNs (SUVmax of the most intense LN, and product of diameters of the largest LN) and primary bladder tumor (product of diameters). In the validation set, diagnostic performance did not differ significantly between the combination of criteria (AUC = 0.59 95%CI [0.43-0.73]) and the consensus of experts (AUC = 0.64 95%CI [0.48-0.78], p = 0.54). The interrater agreement was equally good with Κ = 0.66 for both. CONCLUSION: The developed machine-learning-based combination of criteria performs as well as a consensus of experts to detect pelvic LN involvement on [18F]FDG PET/CT in patients with MIBC. KEY POINTS: • The developed machine-learning-based combination of criteria performs as well as experts to detect pelvic LN involvement on [18F]FDG PET/CT in patients with muscle-invasive bladder cancer. • The top 3 features to predict LN involvement were the SUVmax of the most intense LN, the product of diameters of the largest LN, and the product of diameters of the primary bladder tumor.

High serum LDH and liver metastases are the dominant predictors of primary cancer resistance to anti-PD(L)1 immunotherapy.

AIM: Anti-PD-(L)1 immunotherapies improve survival in multiple cancers but remain ineffective for most patients. We applied machine-learning algorithms and multivariate analyses on baseline medical data to estimate their relative impact on overall survival (OS) upon anti-PD-(L)1 monotherapies. METHOD: This prognostic/predictive study retrospectively analysed 33 baseline routine medical variables derived from computed tomography (CT) images, clinical and biological meta-data. 695 patients with a diagnosis of advanced cancer were treated in prospective clinical trials in a single tertiary cancer centre in 3 cohorts including systemic anti-PD-(L)1 (251, 235 patients) versus other systemic therapies (209 patients). A random forest model combined variables to identify the combination (signature) which best estimated OS in patients treated with immunotherapy. The performance for estimating OS [95%CI] was measured using Kaplan-Meier Analysis and Log-Rank test. RESULTS: Elevated serum lactate dehydrogenase (LDHhi) and presence of liver metastases (LM+) were dominant and independent predictors of short OS in independent cohorts of melanoma and non-melanoma solid tumours. Overall, LDHhiLM+ patients treated with anti-PD-(L)1 monotherapy had a poorer outcome (median OS: 3.1[2.4-7.8] months]) compared to LDHlowLM-patients (median OS: 15.3[8.9-NA] months; P < 0.0001). The OS of LDHlowLM-patients treated with immunotherapy was 28.8[17.9-NA] months (vs 13.1[10.8-18.5], P = 0.02) in the overall population and 30.3[19.93-NA] months (vs 14.1[8.69-NA], P = 0.0013) in patients with melanoma. CONCLUSION: LDHhiLM+ status identifies patients who shall not benefit from anti-PD-(L)1 monotherapy. It could be used in clinical trials to stratify patients and eventually address this specific medical need.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology's role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57-180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10-16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA.

Current radiographic methods of measuring treatment response for patients with nonsmall cell lung cancer have significant limitations. Recently, new modalities using standard of care images or minimally invasive blood-based DNA tests have gained interest as methods of evaluating treatment response. This article highlights three emerging modalities: radiomic analysis, kinetic analysis and serum-based measurement of circulating tumor DNA, with a focus on the clinical evidence supporting these methods. Additionally, we discuss the possibility of combining these modalities to develop a robust biomarker with strong correlation to clinically meaningful outcomes that could impact clinical trial design and patient care. At Last, we focus on how these methods specifically apply to a Veteran population.