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Background and purposeThe complex MR imaging appearance of glioblastoma is a function of underlying histopathologic heterogeneity. A better understanding of these correlations, particularly the influence of infiltrating glioma cells and vasogenic edema on T2 and diffusivity signal in nonenhancing areas, has important implications in the management of these patients. With localized biopsies, the objective of this study was to generate a model capable of predicting cellularity at each voxel within an entire tumor volume as a function of signal intensity, thus providing a means of quantifying tumor infiltration into surrounding brain tissue.Materials and methodsNinety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. Signal intensity was plotted against cell density for each MR image.ResultsT2-FLAIR (r = -0.61) and ADC (r = -0.63) sequences were inversely correlated with cell density. T1-postcontrast (r = 0.69) subtraction was directly correlated with cell density. Combining these relationships yielded a multiparametric model with improved correlation (r = 0.74), suggesting that each sequence offers different and complementary information.ConclusionsUsing localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.

Background and purposeA limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor.Materials and methodsThis retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques.ResultsTwenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and

Objectives: To assess the incremental value of diffusion-weighted (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) to T2-weighted MRI (T2WI) in detecting locally recurrent prostate cancer after radiotherapy.Methods: Twenty-four patients (median age, 70 years) with a history of radiotherapy-treated prostate cancer underwent multi-parametric MRI (MP-MRI) and transrectal prostate biopsy. Two readers independently scored the likelihood of cancer on a 1-5 scale, using T2WI alone and then adding DW-MRI and DCE-MRI. Areas under receiver operating characteristic curves (AUCs) were estimated at the patient and prostate-side levels. The apparent diffusion coefficient (ADC) from DW-MRI and the K(trans), k(ep), v(e), AUGC90 and AUGC180 from DCE-MRI were recorded.Results: Biopsy was positive in 16/24 (67%) and negative in 8/24 (33%) patients. AUCs for readers 1 and 2 increased from 0.64 and 0.53 to 0.95 and 0.86 with MP-MRI, at the patient level, and from 0.73 and 0.66 to 0.90 and 0.79 with MP-MRI, at the prostate-side level (p values < 0.05). Biopsy-positive and biopsy-negative prostate sides differed significantly in median ADC [1.44 vs. 1.68 (×10(-3) mm(2)/s)], median K(trans) [1.07 vs. 0.34 (1/min)], and k(ep) [2.06 vs 1.0 (1/min)] (p values < 0.05).Conclusions: MP-MRI was significantly more accurate than T2WI alone in detecting locally recurrent prostate cancer after radiotherapy. [ABSTRACT FROM AUTHOR]

Background: We sought to determine the response rate and toxicity profile of sequential paclitaxel and bryostatin-1, a novel, selective inhibitor of protein kinase C, in patients with advanced esophageal cancer.Patients and Methods: Patients with advanced esophageal and gastroesophageal junction cancer were enrolled. All gave informed consent. They were initially treated with paclitaxel 90 mg/m(2) intravenously on Day 1 and bryostatin-1 50 microg/m2 on Day 2 weekly for three consecutive weeks out of four. Because of severe myalgias, dosing was reduced to paclitaxel 80 mg/m2 with bryostatin-1 40 microg/m2 and then to paclitaxel 80 mg/m2 with bryostatin-1 25 microg/m2.Results: Twenty-four patients were enrolled, with 22 assessable for response. The partial response rate was 27%. 10 patients treated with bryostatin-1 40-50 microg/m2 had a response rate of 40 versus 17% at bryostatin-1 25 microg/m2 (p-value = 0.3). Median time-to-progression was 3.7 months and median survival was 8.3 months. Grade 3/4 myalgias were seen in 50% of patients. Myalgias appeared to be related to bryostatin-1 dose. Because of toxicity, the trial was closed prior to full accrual.Conclusions: Despite potential anti-tumor activity of this combination in patients with advanced esophageal cancer, further development is not warranted, given the severe toxicity, especially myalgias, that were seen. [ABSTRACT FROM AUTHOR]

New guidelines to evaluate the response to treatment in solid tumors using imaging techniques have major limitations and important implications for radiological workload. This consensus statement from the International Cancer Imaging Society (ICIS) reviews the RECIST criteria and addresses several challenges regarding tumour measurement. Recommendations are made regarding tumour measurement and other issues are raised. The growing need to introduce a multimodality approach to monitoring response is recognized. ICIS welcomes a dialogue with the authors of RECIST to address issues raised in this review.British Journal of Cancer (2004) 90, 2256-2260. doi:10.1038/sj.bjc.6601843 www.bjcancer.com Published online 11 May 2004 [ABSTRACT FROM AUTHOR]

BACKGROUND: Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. FUTURE WORK: A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies. [ABSTRACT FROM AUTHOR]

Purpose: To assess the reproducibility of radiomic features (RFs) extracted from dynamic contrast-enhanced computed tomography (DCE-CT) scans of patients diagnosed with hepatocellular carcinoma (HCC) with regards to inter-observer variability and acquisition timing after contrast injection. The predictive ability of reproducible RFs for differentiating between the degrees of HCC differentiation is also investigated., Methods: We analyzed a set of DCE-CT scans of 39 patients diagnosed with HCC. Two radiologists independently segmented the scans, and RFs were extracted from each sequence of the DCE-CT scans. The same lesion was segmented across the DCE-CT sequences of each patient's scan. From each lesion, 127 commonly used RFs were extracted. The reproducibility of RFs was assessed with regard to (i) inter-observer variability, by evaluating the reproducibility of RFs between the two radiologists; and (ii) timing of acquisition following contrast injection (inter- and intra-imaging phase). The reproducibility of RFs was assessed using the concordance correlation coefficient (CCC), with a cut-off value of 0.90. Reproducible RFs were used for building XGBoost classification models for the differentiation of HCC differentiation., Results: Inter-observer analyses across the different contrast-enhancement phases showed that the number of reproducible RFs was 29 (22.8%), 52 (40.9%), and 36 (28.3%) for the non-contrast enhanced, late arterial, and portal venous phases, respectively. Intra- and inter-sequence analyses revealed that the number of reproducible RFs ranged between 1 (0.8%) and 47 (37%), inversely related with time interval between the sequences. XGBoost algorithms built using reproducible RFs in each phase were found to be high predictive ability of the degree of HCC tumor differentiation., Conclusions: The reproducibility of many RFs was significantly impacted by inter-observer variability, and a larger number of RFs were impacted by the difference in the time of acquisition after contrast injection. Our findings highlight the need for quality assessment to ensure that scans are analyzed in the same physiologic imaging phase in quantitative imaging studies, or that phase-wide reproducible RFs are selected. Overall, the study emphasizes the importance of reproducibility and quality control when using RFs as biomarkers for clinical applications., Competing Interests: Authors acknowledge financial support from the National Institutes of Health (U01 CA225431). The content is solely the responsibility of the authors and does not necessarily represent the views of the funding source. The authors have no relevant financial or non-financial interests to disclose. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Ibrahim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Data collected from hospitals are usually partially annotated by radiologists due to time constraints. Developing and evaluating deep learning models on these data may result in over or under estimation PURPOSE: We aimed to quantitatively investigate how the percentage of annotated lesions in CT images will influence the performance of universal lesion detection (ULD) algorithms., Methods: We trained a multi-view feature pyramid network with position-aware attention (MVP-Net) to perform ULD. Three versions of the DeepLesion dataset were created for training MVP-Net. Original DeepLesion Dataset (OriginalDL) is the publicly available, widely studied DeepLesion dataset that includes 32 735 lesions in 4427 patients which were partially labeled during routine clinical practice. Enriched DeepLesion Dataset (EnrichedDL) is an enhanced dataset that features fully labeled at one or more time points for 4145 patients with 34 317 lesions. UnionDL is the union of the OriginalDL and EnrichedDL with 54 510 labeled lesions in 4427 patients. Each dataset was used separately to train MVP-Net, resulting in the following models: OriginalCNN (replicating the original result), EnrichedCNN (testing the effect of increased annotation), and UnionCNN (featuring the greatest number of annotations)., Results: Although the reported mean sensitivity of OriginalCNN was 84.3% using the OriginalDL testing set, the performance fell sharply when tested on the EnrichedDL testing set, yielding mean sensitivities of 56.1%, 66.0%, and 67.8% for OriginalCNN, EnrichedCNN, and UnionCNN, respectively. We also found that increasing the percentage of annotated lesions in the training set increased sensitivity, but the margin of increase in performance gradually diminished according to the power law., Conclusions: We expanded and improved the existing DeepLesion dataset by annotating additional 21 775 lesions, and we demonstrated that using fully labeled CT images avoided overestimation of MVP-Net's performance while increasing the algorithm's sensitivity, which may have a huge impact to the future CT lesion detection research. The annotated lesions are at https://github.com/ComputationalImageAnalysisLab/DeepLesionData., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

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., (Copyright © 2024 SIR. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 The Authors; Published by the American Association for Cancer Research.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Guha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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)., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

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.

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 T 2 w 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.

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.

[ 18 F]-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.

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.

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.

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., (Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Objectives: Initial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [ 18 F]FDG PET/CT provides insufficient and variable diagnostic performance. We aimed to develop and validate a machine-learning-based combination of criteria on [ 18 F]FDG PET/CT to accurately identify pelvic LN involvement in bladder cancer patients., Methods: Consecutive patients with localized MIBC who performed preoperative [ 18 F]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 [ 18 F]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 [ 18 F]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., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

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., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

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 (LDH hi ) 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, LDH hi LM+ patients treated with anti-PD-(L)1 monotherapy had a poorer outcome (median OS: 3.1[2.4-7.8] months]) compared to LDH low LM-patients (median OS: 15.3[8.9-NA] months; P < 0.0001). The OS of LDH low LM-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: LDH hi LM+ 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., Competing Interests: Conflict of interest statement Mithat Gönen. Consulting or Advisory Role: Tesaro. Aurelien Marabelle. In the last 5 years, consulting and/or participation to scientific advisory boards with the following companies developing or commercialising anti-PD(L)1 antibodies: BMS, MSD, Roche/Genentech, Astra Zeneca, Pfizer, Sanofi, Novartis, Merck Serono, Symphogen, Servier, GSK. Lawrence H. Schwartz. Consulting or Advisory Role: Novartis, Regeneron. Research Funding: Merck Sharp & Dohme (Inst), Pfizer (Inst), BMS (Inst). Patents, Royalties, Other Intellectual Property: Varian Medical Systems. Jean-Charles Soria. In the last 3 years, consultancy fees from: AstraZeneca, Astex, Clovis, GSK, GamaMabs, Lilly, MSD, Mission Therapeutics, Merus, Pfizer, PharmaMar, Pierre Fabre, Roche-Genentech, Sanofi, Servier, Symphogen, Takeda. Full time employee for AstraZeneca from September 2017 to December 2019. Shareholder Gritstone. Lambros Tselikas. Grants: Terumo, BMS foundation. Fees, education, lecture: BMS, Boston Scientific, GE, Guerbet, Sirtex. Other authors. None., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Despite considerable advances in preoperative imaging, up to one-third of patients operatively explored for hepatic colorectal metastases are unexpectedly found to harbor unresectable intrahepatic or extrahepatic disease., Methods: The current study is a prospective, blinded study comparing utility of [ 18 F]2-fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG-PET) to computed tomography (CT) and CT arterial portography (CTAP) as preoperative staging., Results: The 125 planned subjects were enrolled. Findings seen on FDG-PET alone changed therapy for 23 of 125 patients (18%). FDG-PET confirmed other radiologic findings in 16 cases (13%), for an overall influence on therapy in 39 cases (31%). FDG-PET was the most sensitive diagnostic imaging test for extrahepatic cancer; it was 80-90% sensitive for extrahepatic cancer and 70-90% specific. For the 28 cases of unresectable disease due to extrahepatic disease, FDG-PET findings solely changed therapies in 16 cases (57%) and influenced therapy in seven other cases (25%). Of the 21 unresectable cases due to extent of intrahepatic disease, FDG-PET did not solely change therapy in any. Overall, FDG-PET had the lowest sensitivity for hepatic sites compared with CT or CTAP. In particular, small (<1 cm) liver tumors were particularly poorly detected by FDG-PET. The area under the receiver operating characteristic (ROC) curve for small tumors was 0.58 and for patients on chemotherapy it was 0.66, a modest improvement over no imaging., Conclusions: FDG-PET is an important test for preoperative staging of patients with hepatic colorectal metastases, affecting treatment decisions in nearly one-third of patients. The high yield is due mainly to detection of extrahepatic disease. It is therefore recommended in patients with extrahepatic lesions suspected to be disseminated cancer or those with high risk for extrahepatic disease. It is not a good test for identification of small tumors in the liver., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-19-357/coif). YF serves as an unpaid editorial board member of Hepatobiliary Surgery and Nutrition. The other authors have no conflicts of interest to declare., (2022 Hepatobiliary Surgery and Nutrition. All rights reserved.)

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., Competing Interests: Conflicts of interest Dr. Schwartz - Segmentation algorithm licensed to Varian Medical System., (Published by Elsevier Inc.)

Competing Interests: Competing interests: EGEdV reports institutional financial support for advisory boards/consultancy from NSABP, Daiichi Sankyo and Crescendo Biologics, and institutional financial support for clinical trials or contracted research grants from Amgen, Genentech, Roche, CytomX, G1 Therapeutics, Bayer, Synthon, Servier, Regeneron, Crescendo Biologics, GE Healthcare and AstraZeneca. Regarding non-financial interests, including non-renumerated activities and public positions, EGEdV reports being a member of the ESMO-MCBS working party, chair of the ESMO Cancer Medicines Working Group, co-chair of the RECIST committee, and member of expert panel for selection of Essential Medicine List WHO. LHS reports consulting or serving in an advisory role for Novartis International AG and Regeneron Pharmaceuticals; receiving institutional research funding from Merck Sharp & Dohme, Pfizer, and Bristol Myers Squibb; and having patent US20140029828A1 from Varian Medical Systems. No other disclosures were reported.

Immunotherapy has transformed lung cancer care in recent years. In addition to providing durable responses and prolonged survival outcomes for a subset of patients with heavily pretreated non-small cell lung cancer (NSCLC), immune checkpoint inhibitors (ICIs)- either as monotherapy or in combination with other ICIs or chemotherapy-have demonstrated benefits in first-line therapy for advanced disease, the neoadjuvant and adjuvant settings, as well as in additional thoracic malignancies such as small-cell lung cancer (SCLC) and mesothelioma. Challenging questions remain, however, on topics including therapy selection, appropriate biomarker-based identification of patients who may derive benefit, the use of immunotherapy in special populations such as people with autoimmune disorders, and toxicity management. Patient and caregiver education and support for quality of life (QOL) is also important to attain maximal benefit with immunotherapy. To provide guidance to the oncology community on these and other important concerns, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel of experts to develop a clinical practice guideline (CPG). This CPG represents an update to SITC's 2018 publication on immunotherapy for the treatment of NSCLC, and is expanded to include recommendations on SCLC and mesothelioma. The Expert Panel drew on the published literature as well as their clinical experience to develop recommendations for healthcare professionals on these important aspects of immunotherapeutic treatment for lung cancer and mesothelioma, including diagnostic testing, treatment planning, immune-related adverse events, and patient QOL considerations. The evidence- and consensus-based recommendations in this CPG are intended to give guidance to cancer care providers using immunotherapy to treat patients with lung cancer or mesothelioma., Competing Interests: Competing interests: CA—Consulting fees: AstraZeneca, Celgene, Eli Lilly, Genetech, Merck. SJA—Advisory board: Bristol Myers Squibb, Celsius, Merck, Samyang Biopharma, AstraZeneca/Medimmune; Consultant: Bristol Myers Squibb, Celsius, Merck, Samyang Biopharma, AstraZeneca/Medimmune, CBMG, Memgen, RAPT, Venn, Achilles Therapeutics, GlaxoSmithKline, Amgen; Scientific advisory board: CBMG, Memgen, RAPT, Venn, Achilles Therapeutics, GlaxoSmithKline, Amgen. MD—Speaker bureaus: Genentech, Merck, Bristol Myers Squibb and AstraZeneca (all have been discontinued as of November 2019). SMD—Scientific advisory boards: Johnson & Johnson Scientific, T-Cure Bioscience, LungLife AI, Early Diagnostics; Contracted research: Jansen. PMF—Consulting fees: Amgen, AstraZeneca, Bristol Myers Squibb, Iteos, Novartis, Janssen; Contracted research: AstraZeneca, Bristol Myers Squibb, Kyowa, Novartis; DSMB: Polaris, Flame. EBG—Consulting fees: ABL-Bio, Boehringer Ingelheim, Bristol Myers Squibb, Dracen, EMD Serono, Eisai, GlaxoSmithKline, Merck, Novartis; Contracted research: AstraZeneca, Bristol Myers Squibb, Dynavax, EMD Serono, Eli Lilly, Genentech, Merck, Iovance Biotherapeutics, Mirati Therapeutics, Neon, Novartis. SBG—Consulting fees: AstraZeneca, Blueprint Medicine, Bristol Myers Squibb, Boehringer Ingelheim, Genentech, Sanofi Genzyme, Daiichi Sankyo, Regeneron, Takeda, Janssen; Contracted research: AstraZeneca, Boehringer Ingelheim. RG—Consulting fee: Achilles, GenePlus, Inivata, Roche. RH—Contracted research: My institution receives funds for conducting clinical trials from Bayer and TCR2 under a cooperative research and development agreement with NCI. MDH—Consulting fees: Merck, Bristol Myers Squibb, AstraZeneca, Genentech/Roche, Nektar, Syndax, Mirati, Shattuck Labs, Immunai, Blueprint Medicines, Achilles, Arcus; Contracted research: Bristol Myers Squibb; IP rights: A patent filed by his institution related to the use of tumor mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from PGDx; Ownership interest: Arcus, Shattuck Labs, Immunai. RSH—Consulting fees: AbbVie Pharmaceuticals, ARMO Biosciences, AstraZeneca, Bayer HealthCare Pharmaceuticals, Bolt Biotherapeutics, Bristol Myers Squibb, Candel Therapeutics, Cybrexa Therapeutics, eFFECTOR Therapeutics, Eli Lilly and Company, EMD Serono, Foundation Medicine, Genentech/Roche, Genmab, Gilead, Halozyme Therapeutics, Heat Biologic, I-Mab Biopharma, Immunocore, Infinity Pharmaceuticals, Loxo Oncology, Merck and Company; Mirati Therapeutics, Nektar, Neon Therapeutics, NextCure, Novartis, Ocean Biomedical, Oncternal Therapeutics, Pfizer, Ribbon Therapeutics, Ventana Medical Systems, Sanofi, Seattle Genetics, Shire PLC, Spectrum Pharmaceuticals, STCube Pharmaceuticals, Symphogen, Takeda, Tesaro, Tocagen, WindMIL Therapeutics, Xencor; Contracted research: AstraZeneca, Eli Lilly and Company, Genentech/Roche, Merck and Company; Board member (non-executive/independent): Immunocore Holdings Limited, Junshi Pharmaceuticals. FRH—Consulting fees: Merck, Bristol Myers Squibb, Genentech/Roche, Lilly/Loxo, Daiichi, Novartis, AstraZeneca, Boehringer Ingelheim, OncoCyte, Amgen, Sanofi/Regeneron; Contracted research through University of Colorado: Amgen, AbbVie, Biodesix, Rain Therapeutics, Aurora Oncology, Mersana; IP rights through University of Colorado: EGFR protein and EGFR Gene Copy Number as Predictive Biomarker for EGFR Therapy. MLJ—Consulting fees – all payments made to my institution: Achilles Therapeutics, Amgen, AstraZeneca, Atreca, Boehringer Ingelheim, Bristol Myers Squibb, Daiichi Sankyo, EMD Serono, G1 Therapeutics, Roche/Genentech, GlaxoSmithKline, Gritstone, Incyte, Janssen, Lilly, Merck, Mirati, Novartis, Pfizer, Sanofi-Aventis, WindMIL Therapeutics; Contracted research – all payments made to my institution: AbbVie, Adaptimmune, Amgen, Apexigen, Array Biopharma, AstraZeneca, Atreca, BeiGene, BerGenBio, Boehringer Ingelheim, Calithera, Checkpoint Therapeutics, Corvus Pharmaceuticals, CytomX, Daiichi Sankyo, Lilly, EMD Serono, Roche/Genentech, Genmab, Genocea, GlaxoSmithKline, Gritstone Oncology, Guardant Health, Hengrui Therapeutics, Immunocore, Incyte, Janssen, Jounce, Mirati, Neovia Oncology, Novartis, Pfizer, Regeneron, Sanofi, Shattuck Labs, Syndax, Takeda, Tarveda, University of Michigan, WindMIL Therapeutics, TCR2 Therapeutics, Arcus, Ribon, Rubius Therapeutics, Tmunity Therapeutics, Seven and Eight Biosciences, Foundation Medicine; Partner consulting Fees: Otsuka. DM—Consulting fees: Gilead, G1 Therapeutics, AbbVie. JWN—Grants and personal fees: Takeda (during the conduct of the study), AstraZeneca; Grants (non-financial support): Genentech/Roche, Exelixis, Jounce Therapeutics, Eli Lilly and Company, Calithera Biosciences, Amgen, Iovance Biotherapeutics, Blueprint Pharmaceuticals, Regeneron Pharmaceuticals, Natera; Grants and non-financial support: Merck, Novartis, Boehringer Ingelheim, Nektar Therapeutics, Adaptimmune, GSK, Janssen, AbbVie. JDP—Consulting fees: AstraZeneca, AbbVie, Takeda; Contracted research: Bristol Myers Squibb, Takeda, RAIN. DLR—Consulting fees: AstraZeneca, Agendia, Amgen, BMS, Cell Signaling Technology, Cepheid, Danaher, Daiichi Sankyo, Genoptix/Novartis, GSK, Konica Minolta, Merck, NanoString, PAIGE.AI, Perkin Elmer, Roche, Sanofi, Ventana and Ultivue; Laboratory support: Amgen, Cepheid, NavigateBP, NextCure, and Konica Minolta. SS—Gift cards: Healthcasts-Syneos, ZOOM RX Surveys, Schlesinger Group, Impact Rx; Monetary compensation: Global M3. LHS—Contracted research: Merck, Bristol Myers Squibb, Regeneron. BS—Consulting fees: Bristol Myers Squibb. SITC staff: SMW—Shares owned: Pacific Biosciences, Editas Medicine; AK, LL, CG--Nothing to disclose, (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Accurate prediction of portal hypertension recurrence after transjugular intrahepatic portosystemic shunt (TIPS) placement will improve clinical decision-making., Purpose: To evaluate if perioperative variables could predict disease-free survival (DFS) in cirrhotic patients with portal hypertension (PHT) treated with TIPS., Materials and Methods: We recruited 206 cirrhotic patients with PHT treated with TIPS, randomly assigned to training (n = 138) and validation (n = 68) sets. We recorded 7 epidemiological, 4 clinical, and 9 radiological variables. TIPS-distal end positioning (TIPS-DEP) measured the distance between the distal end of the stent and the hepatocaval junction on contrast-enhanced CT scans. In the training set, the signature was defined as the random forest for survival algorithm achieving the lowest error rate for the prediction of DFS which was landmarked 4 weeks after the TIPS procedure. In the training set, a simple to use scoring system was derived from variables selected by the signature. The primary endpoint was to assess if TIPS-DEP was associated with DFS. The secondary endpoint was to validate the scoring system in the validation set., Results: Overall, patients with TIPS-DEP ≥ 6 mm (n = 49) had a median DFS of 24.5 months vs. 72.8 months otherwise (n = 157, p = 0.004). In the training set, the scoring system was calculated by adding age ≥ 60 years old, Child-Pugh B or C, and TIPS-DEP ≥ 6 mm (1 point each) since the signature showed high DFS probability at 6.5 months post-landmark in patients that did not meet these criteria: 86%, 80%, and 78%, respectively. The hazard ratio [95 CI] between patients determined to be low-risk (< 2 points) and high-risk (≥ 2 points) was 2.30 [1.35-3.93] (p = 0.002) in the training set and 2.01 [0.94-4.32] (p = 0.072) in the validation set., Conclusion: TIPS-DEP is an actionable radiological biomarker which can be combined with age and Child-Pugh score to predict death or PHT symptom recurrence after TIPS procedure., Key Points: • TIPS-DEP measurement was the third most important but only actionable variable for predicting DFS. • TIPS-DEP < 6 mm was associated with a DFS probability of 78% at 6.5 months post-landmark. • A simple scoring system calculated using age, Child-Pugh score, and TIPS-DEP predicted DFS after TIPS., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Importance: Existing criteria to estimate the benefit of a therapy in patients with cancer rely almost exclusively on tumor size, an approach that was not designed to estimate survival benefit and is challenged by the unique properties of immunotherapy. More accurate prediction of survival by treatment could enhance treatment decisions., Objective: To validate, using radiomics and machine learning, the performance of a signature of quantitative computed tomography (CT) imaging features for estimating overall survival (OS) in patients with advanced melanoma treated with immunotherapy., Design, Setting, and Participants: This prognostic study used radiomics and machine learning to retrospectively analyze CT images obtained at baseline and first follow-up and their associated clinical metadata. Data were prospectively collected in the KEYNOTE-002 (Study of Pembrolizumab [MK-3475] Versus Chemotherapy in Participants With Advanced Melanoma; 2017 analysis) and KEYNOTE-006 (Study to Evaluate the Safety and Efficacy of Two Different Dosing Schedules of Pembrolizumab [MK-3475] Compared to Ipilimumab in Participants With Advanced Melanoma; 2016 analysis) multicenter clinical trials. Participants included 575 patients with a diagnosis of advanced melanoma who were randomly assigned to training and validation sets. Data for the present study were collected from November 20, 2012, to June 3, 2019, and analyzed from July 1, 2019, to September 15, 2021., Interventions: KEYNOTE-002 featured trial groups testing intravenous pembrolizumab, 2 mg/kg or 10 mg/kg every 2 or every 3 weeks based on randomization, or investigator-choice chemotherapy; KEYNOTE-006 featured trial groups testing intravenous ipilimumab, 3 mg/kg every 3 weeks and intravenous pembrolizumab, 10 mg/kg every 2 or 3 weeks based on randomization., Main Outcomes and Measures: The performance of the signature CT imaging features for estimating OS at the month 6 posttreatment landmark in patients who received pembrolizumab was measured using an area under the time-dependent receiver operating characteristics curve (AUC)., Results: A random forest model combined 25 imaging features extracted from tumors segmented on CT images to identify the combination (signature) that best estimated OS with pembrolizumab in 575 patients. The signature combined 4 imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype. In the validation set (287 patients treated with pembrolizumab), the signature reached an AUC for estimation of OS status of 0.92 (95% CI, 0.89-0.95). The standard method, Response Evaluation Criteria in Solid Tumors 1.1, achieved an AUC of 0.80 (95% CI, 0.75-0.84) and classified tumor outcomes as partial or complete response (93 of 287 [32.4%]), stable disease (90 of 287 [31.3%]), or progressive disease (104 of 287 [36.2%])., Conclusions and Relevance: The findings of this prognostic study suggest that the radiomic signature discerned from conventional CT images at baseline and on first follow-up may be used in clinical settings to provide an accurate early readout of future OS probability in patients with melanoma treated with single-agent programmed cell death 1 blockade.

Objectives: To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size., Methods: We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets., Results: The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models., Conclusions: For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially., Key Points: • CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1 cm. • In pulmonary nodules less than 1 cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1 cm, CT image acquisition parameters do not affect diagnostic performance substantially., (© 2021. European Society of Radiology.)

Background & Aims: Quantitative analysis of computed tomography (CT) scans of patients with metastatic colorectal cancer (mCRC) can identify imaging signatures that predict overall survival (OS)., Methods: We retrospectively analysed CT images from 1584 mCRC patients on two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466). In the training set (n = 720), an algorithm was trained to predict OS landmarked from month 2; the output was a signature value on a scale from 0 to 1 (most to least favourable predicted OS). In the validation set (n = 864), hazard ratios (HRs) evaluated the association of the signature with OS using RECIST1.1 as a benchmark of comparison., Results: In the training set, the selected signature combined three features - change in tumour volume, change in tumour spatial heterogeneity, and tumour volume - to predict OS. In the validation set, RECIST1.1 classified patients in three categories: response (n = 166, 19.2%), stable disease (n = 636, 73.6%), and progression (n = 62, 7.2%). The HR was 3.93 (2.79-5.54). Using the same distribution for the signature, the HR was 21.04 (14.88-30.58), showing an incremental prognostic separation. Stable disease by RECIST1.1 was reclassified by the signature along a continuum where patients belonging to the most and least favourable signature quartiles had a median OS of 40.73 (28.49 to NA) months (n = 94) and 7.03 (5.66-7.89) months (n = 166), respectively., Conclusions: A signature combining three imaging features provides early prognostic information that can improve treatment decisions for individual patients and clinical trial analyses., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.

In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier than tumor size change. Here we present an analysis utilizing a deep learning (DL) network to characterize tumor morphological change for response assessment in mCRC patients. We retrospectively analyzed 1,028 mCRC patients who were prospectively included in the VELOUR trial (NCT00561470). We found that DL network was able to predict early on-treatment response in mCRC and showed better performance than its size-based counterpart with C-Index: 0.649 (95% CI: 0.619,0.679) vs. 0.627 (95% CI: 0.567,0.638), p = 0.009, z-test. The integration of DL network with size-based methodology could further improve the prediction performance to C-Index: 0.694 (95% CI: 0.661,0.720), which was superior to size/DL-based-only models (all p < 0.001, z-test). Our study suggests that DL network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making., (© 2021. The Author(s).)

The clinical deployment of artificial intelligence (AI) applications in medical imaging is perhaps the greatest challenge facing radiology in the next decade. One of the main obstacles to the incorporation of automated AI-based decision-making tools in medicine is the failure of models to generalize when deployed across institutions with heterogeneous populations and imaging protocols. The most well-understood pitfall in developing these AI models is overfitting, which has, in part, been overcome by optimizing training protocols. However, overfitting is not the only obstacle to the success and generalizability of AI. Underspecification is also a serious impediment that requires conceptual understanding and correction. It is well known that a single AI pipeline, with prescribed training and testing sets, can produce several models with various levels of generalizability. Underspecification defines the inability of the pipeline to identify whether these models have embedded the structure of the underlying system by using a test set independent of, but distributed identically, to the training set. An underspecified pipeline is unable to assess the degree to which the models will be generalizable. Stress testing is a known tool in AI that can limit underspecification and, importantly, assure broad generalizability of AI models. However, the application of stress tests is new in radiologic applications. This report describes the concept of underspecification from a radiologist perspective, discusses stress testing as a specific strategy to overcome underspecification, and explains how stress tests could be designed in radiology-by modifying medical images or stratifying testing datasets. In the upcoming years, stress tests should become in radiology the standard that crash tests have become in the automotive industry. Keywords: Computer Applications-General, Informatics, Computer-aided Diagnosis © RSNA, 2021., Competing Interests: Disclosures of conflicts of interest: T.E. No relevant relationships. L.H.S. Consultant for Merck and Regeneron (member, DSMB and endpoint analysis committees). F.Z.M. No relevant relationships. L.D. No relevant relationships., (2021 by the Radiological Society of North America, Inc.)