A Phase I Trial of Image-Guided, Risk-Volume-Adapted Post-Prostatectomy Radiotherapy.

PURPOSE: This is a phase I trial with the primary objective of identifying the most compressed dose schedule (DS) tolerable using risk-volume-adapted, hypofractionated, post-operative radiotherapy (PORT) for biochemically recurrent prostate cancer. Secondary endpoints included biochemical progression free survival (bPFS) and quality of life (QOL). METHODS: Patients were treated with one of 3 isoeffective dose schedules (DS1: 20 fractions, DS2: 15 fractions, DS3: 10 fractions) that escalated dose to the imaging-defined local recurrence (73Gy3 EQD2) and de-escalated dose to the remainder of the prostate bed (48Gy3 EQD2). Escalation followed a standard 3+3 design with a 6-patient expansion at the maximally tolerated hypofractionated dose schedule (MTHDS). Dose limiting toxicity (DLT) was defined as CTCAE v.4.0 grade (G) 3 toxicity lasting >4 days within 21 days of PORT completion or grade 4 gastrointestinal (GI) or genitourinary (GU) toxicities thereafter. QOL was assessed longitudinally through 24 months with the EPIC-26. RESULTS: Between 01/2018 and 12/2023, 15 patients were treated (3 with DS1, 3 with DS2, and 9 with DS3). The median follow-up was 48 months. No DLTs were observed on any DS, and, thus, expansion occurred at DS3. The cumulative incidence of G3 GI and GU toxicity was 7% and 9% at 24 months, respectively, with no G4 events observed. Transient, acute G2+ GI toxicity was most common. QOL worsened transiently during study follow-up in urinary incontinence, GI, and sexual subdomains but was similar to baseline by 24 months. The bPFS was 91% at both 24- and 60-months. CONCLUSIONS: The maximally tolerated hypofractionated dose schedule for hypofractionated, risk-volume-adapted PORT was determined to be DS3 (36.4Gy to the prostate bed and 47.1Gy to the imaging-defined recurrence in 10 daily fractions). No >G3 events were observed. Transient declines in QOL did not persist through 24 months.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review.

The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.

A Phase 1 Trial of Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer After Brachytherapy.

PURPOSE: NCT03253744 is a phase 1 trial with the primary objective to identify the maximum tolerated dose (MTD) of salvage stereotactic body radiation therapy (SBRT) in patients with local prostate cancer recurrence after brachytherapy. Additional objectives included biochemical control and imaging response. METHODS AND MATERIALS: This trial was initially designed to test 3 therapeutic dose levels (DLs): 40 Gy (DL1), 42.5 Gy (DL2), and 45 Gy (DL3) in 5 fractions. Intensity modulation was used to deliver the prescription dose to the magnetic resonance imaging and prostate-specific membrane antigen-based positron emission tomography imaging-defined gross tumor volume while simultaneously delivering 30 Gy to an elective volume defined by the prostate gland. This phase 1 trial followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities were scored until trial completion at 2 years post-SBRT using Common Terminology Criteria for Adverse Events version 5.0. Escalation was halted if 2 dose limiting toxicities occurred, defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT or any grade ≥3 genitourinary (GU) or grade 4 gastrointestinal toxicity thereafter. RESULTS: Between August 2018 and January 2023, 9 patients underwent salvage SBRT and were observed for a median of 22 months (Q1-Q3, 20-43 months). No grade 3 to 5 adverse events related to study treatment were observed; thus, no dose limiting toxicities occurred during the observation period. Escalation was halted by amendment given excellent biochemical control in DL1 and DL2 in the setting of a high incidence of clinically significant late grade 2 GU toxicity. Therefore, the MTD was considered 42.5 Gy in 5 fractions (DL2). One- and 2-year biochemical progression-free survival were 100% and 86%, representing a single patient in the trial cohort with biochemical failure (prostate-specific antigen [PSA] nadir + 2.0) at 20 months posttreatment. CONCLUSIONS: The MTD of salvage SBRT for the treatment of intraprostatic radiorecurrence after brachytherapy was 42.5 Gy in 5 fractions producing an 86% 2-year biochemical progression-free survival rate, with 1 poststudy failure at 20 months. The most frequent clinically significant toxicity was late grade 2 GU toxicity.

A Phase 1 Trial of Focal Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer.

PURPOSE: NCT03253744 was a phase 1 trial to identify the maximum tolerated dose (MTD) of image-guided, focal, salvage stereotactic body radiation therapy (SBRT) for patients with locally radiorecurrent prostate cancer. Additional objectives included biochemical control and imaging response. METHODS AND MATERIALS: The trial design included 3 dose levels (DLs): 40 Gy (DL1), 42.5 Gy (DL2), and 45 Gy (DL3) in 5 fractions delivered ≥48 hours apart. The prescription dose was delivered to the magnetic resonance- and prostate-specific membrane antigen imaging-defined tumor volume. Dose escalation followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities were scored until 2 years after completion of SBRT using Common Terminology Criteria for Adverse Events, version 5.0, criteria. Escalation was halted if 2 dose-limiting toxicities occurred, defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT and any grade 3 genitourinary (GU) or grade 4 gastrointestinal (GI) toxicity thereafter. RESULTS: Between August 2018 and May 2022, 8 patients underwent salvage focal SBRT, with a median follow-up of 35 months. No dose-limiting toxic effects were observed on DL1. Two patients were enrolled in DL2 and experienced grade 3 GU toxicities, prompting de-escalation and expansion (n = 6) at the MTD (DL1). The most common toxicities observed were grade ≥2 GU toxicities, with only a single grade 2 GI toxicity and no grade ≥3 GI toxicities. One patient experienced biochemical failure (prostate-specific antigen nadir + 2.0) at 33 months. CONCLUSIONS: The MTD for focal salvage SBRT for isolated intraprostatic radiorecurrence was 40 Gy in 5 fractions, producing a 100% 24-month biochemical progression free survival, with 1 poststudy failure at 33 months. The most frequent clinically significant toxicity was late grade ≥2 GU toxicity.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Criteria for the translation of radiomics into clinically useful tests.

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

Early drug development in solid tumours: analysis of National Cancer Institute-sponsored phase 1 trials.

BACKGROUND: The low expectation of clinical benefit from phase 1 cancer therapeutics trials might negatively affect patient and physician participation, study reimbursement, and slow the progress of oncology research. Advances in cancer drug development, meanwhile, might have favourably improved treatment responses; however, little comprehensive data exist describing the response and toxicity associated with phase 1 trials across solid tumours. The aim of the study is to evaluate the trend of toxicity and response in phase 1 trials for solid tumours over time. METHODS: We analysed patient-level data from the Cancer Therapy Evaluation Program of the National Cancer Institute-sponsored investigator-initiated phase 1 trials for solid tumours, from Jan 1, 2000, to May 31, 2019. We assessed risks of treatment-related death (grade 5 toxicity ratings possibly, probably, or definitely attributable to treatment), all on-treatment deaths (deaths during protocol treatment regardless of attribution), grade 3-4 toxicity, and proportion of overall response (complete response and partial response) and complete response rate in the study periods of 2000-05, 2006-12, and 2013-2019, and evaluated their trends over time. We also analysed cancer type-specific and investigational agent-specific response, and analysed the trend of response in each cancer type over time. Univariate associations of overall response rates with patients' baseline characteristics (age, sex, performance status, BMI, albumin concentration, and haemoglobin concentration), enrolment period, investigational agents, and trial design were assessed using risk ratio based on the modified Poisson regression model. FINDINGS: We analysed 465 protocols that enrolled 13 847 patients using 261 agents. 144 (31%) trials used a monotherapy and 321 (69%) used combination therapies. The overall treatment-related death rate was 0·7% (95% CI 0·5-0·8) across all periods. Risks of treatment-related deaths did not change over time (p=0·52). All on-treatment death risk during the study period was 8·0% (95% CI 7·6-8·5). The most common grade 3-4 adverse events were haematological; grade 3-4 neutropenia occurred in 2336 (16·9%) of 13 847 patients, lymphopenia in 1230 (8·9%), anaemia in 894 (6·5%), and thrombocytopenia in 979 (7·1%). The overall response rate for all trials during the study period was 12·2% (95% CI 11·5-12·8; 1133 of 9325 patients) and complete response rate was 2·7% (2·4-3·0; 249 of 9325). Overall response increased from 9·6% (95% CI 8·7-10·6) in 2000-05 to 18·0% (15·7-20·5) in 2013-19, and complete response rates from 2·5% (2·0-3·0) to 4·3% (3·2-5·7). Overall response rates for combination therapy were substantially higher than for monotherapy (15·8% [15·0-16·8] vs 3·5% [2·8-4·2]). The overall response by class of agents differed across diseases. Anti-angiogenesis agents were associated with higher overall response rate for bladder, colon, kidney and ovarian cancer. DNA repair inhibitors were associated with higher overall response rate in ovarian and pancreatic cancer. The rates of overall response over time differed markedly by disease; there were notable improvements in bladder, breast, and kidney cancer and melanoma, but no change in the low response of pancreatic and colon cancer. INTERPRETATION: During the past 20 years, the response rate in phase 1 trials nearly doubled without an increase in the treatment-related death rate. However, there is significant heterogeneity in overall response by various factors such as cancer type, investigational agent, and trial design. Therefore, informed decision making is crucial for patients before participating in phase 1 trials. This study provides updated encouraging outcomes of modern phase 1 trials in solid tumours. FUNDING: National Cancer Institute.

Trends in Grade 5 Toxicity and Response in Phase I Trials in Hematologic Malignancy: 20-Year Experience From the Cancer Therapy Evaluation Program at the National Cancer Institute.

PURPOSE: Cancer drug development has largely shifted from cytotoxic chemotherapy to targeted treatment in the past two decades. Although previous studies have highlighted improvement in response rates in recent phase I trials, disease-focused reporting is limited. METHODS: We integrated patient-level data for patients with hematologic malignancies who participated in phase I trials sponsored by the National Cancer Institute Cancer Therapy Evaluation Program between January 2000 and May 2019 and estimated the trend of grade 5 toxicity and response by disease subtype over time. RESULTS: We analyzed 161 trials involving 3,308 patients, all of whom were assessed for toxicity and 2,404 of whom were evaluable for response to therapy. The overall rate of grade 5 toxicities was 1.81% (95% CI, 1.36 to 2.27), with no significant change in the rate over time. Baseline characteristics associated with higher risk of grade 5 toxicity were age and performance status ≥ 2 at enrollment. Overall response rate (ORR) and complete response (CR) rate for all trials during the study period were 25.1% and 14.7%, respectively. A significant increase in both ORR and CR rate was observed over time (ORR, 18.5% in 2000-2005, 25.9% in 2006-2012, and 50.6% in 2013-2019, P < .001). ORR in phase I trials varied across disease subtypes: 20.2% in acute myeloid leukemia, 9.1% in myelodysplastic syndrome, 43.2% in lymphoma, 42.9% in chronic lymphocytic leukemia, 15.1% in acute lymphoblastic leukemia, and 16.5% in myeloma. CONCLUSION: Over time, the ORR and CR rates in phase I trials for hematologic malignancy have improved meaningfully, whereas the rate of toxicity-related death remains stable. This study provides broad experience that physicians can use when discussing the potential outcomes for patients with hematologic malignancy considering participation in phase I trials.

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma.

Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials.

Factors Affecting Combination Trial Success (FACTS): Investigator Survey Results on Early-Phase Combination Trials.

Experimental therapeutic oncology agents are often combined to circumvent tumor resistance to individual agents. However, most combination trials fail to demonstrate sufficient safety and efficacy to advance to a later phase. This study collected survey data on phase 1 combination therapy trials identified from ClinicalTrials.gov between January 1, 2003 and November 30, 2017 to assess trial design and the progress of combinations toward regulatory approval. Online surveys (N = 289, 23 questions total) were emailed to Principal Investigators (PIs) of early-phase National Cancer Institute and/or industry trials; 263 emails (91%) were received and 113 surveys completed (43%). Among phase 1 combination trials, 24.9% (95%CI: 15.3%, 34.4%) progressed to phase 2 or further; 18.7% (95%CI: 5.90%, 31.4%) progressed to phase 3 or regulatory approval; and 12.4% (95%CI: 0.00%, 25.5%) achieved regulatory approval. Observations of "clinical promise" in phase 1 combination studies were associated with higher rates of advancement past each milestone toward regulatory approval (cumulative OR = 11.9; p = 0.0002). Phase 1 combination study designs were concordant with Clinical Trial Design Task Force (CTD-TF) Recommendations 79.6% of the time (95%CI: 72.2%, 87.1%). Most discordances occurred where no plausible pharmacokinetic or pharmacodynamic interactions were expected. Investigator-defined "clinical promise" of a combination is associated with progress toward regulatory approval. Although concordance between study designs of phase 1 combination trials and CTD-TF Recommendations was relatively high, it may be beneficial to raise awareness about the best study design to use when no plausible pharmacokinetic or pharmacodynamic interactions are expected.

Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma.

BACKGROUND AND PURPOSE: Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. MATERIALS AND METHODS: Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. RESULTS: The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. CONCLUSION: Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group.

Purpose To evaluate interradiologist agreement on assessments of computed tomography (CT) imaging features of high-grade serous ovarian cancer (HGSOC), to assess their associations with time-to-disease progression (TTP) and HGSOC transcriptomic profiles (Classification of Ovarian Cancer [CLOVAR]), and to develop an imaging-based risk score system to predict TTP and CLOVAR profiles. Materials and Methods This study was a multireader, multi-institutional, institutional review board-approved, HIPAA-compliant retrospective analysis of 92 patients with HGSOC (median age, 61 years) with abdominopelvic CT before primary cytoreductive surgery available through the Cancer Imaging Archive. Eight radiologists from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group developed and independently recorded the following CT features: characteristics of primary ovarian mass(es), presence of definable mesenteric implants and infiltration, presence of other implants, presence and distribution of peritoneal spread, presence and size of pleural effusions and ascites, lymphadenopathy, and distant metastases. Interobserver agreement for CT features was assessed, as were univariate and multivariate associations with TTP and CLOVAR mesenchymal profile (worst prognosis). Results Interobserver agreement for some features was strong (eg, α = .78 for pleural effusion and ascites) but was lower for others (eg, α = .08 for intraparenchymal splenic metastases). Presence of peritoneal disease in the right upper quadrant (P = .0003), supradiaphragmatic lymphadenopathy (P = .0004), more peritoneal disease sites (P = .0006), and nonvisualization of a discrete ovarian mass (P = .0037) were associated with shorter TTP. More peritoneal disease sites (P = .0025) and presence of pouch of Douglas implants (P = .0045) were associated with CLOVAR mesenchymal profile. Combinations of imaging features contained predictive signal for TTP (concordance index = 0.658; P = .0006) and CLOVAR profile (mean squared deviation = 1.776; P = .0043). Conclusion These results provide some evidence of the clinical and biologic validity of these image features. Interobserver agreement is strong for some features, but could be improved for others. © RSNA, 2017 Online supplemental material is available for this article.

Beyond Correlations, Sensitivities, and Specificities: A Roadmap for Demonstrating Utility of Advanced Imaging in Oncology Treatment and Clinical Trial Design.

Despite the widespread belief that advanced imaging should be very helpful in guiding oncology treatment decision and improving efficiency and success rates in treatment clinical trials, its acceptance has been slow. Part of this is likely attributable to gaps in study design and statistical methodology for these imaging studies. Also, results supporting the performance of the imaging in these roles have largely been insufficient to justify their use within the design of a clinical trial or in treatment decision making. Statistically significant correlations between the imaging results and clinical outcomes are often incorrectly taken as evidence of adequate performance. Assessments of whether the imaging can outperform standard techniques or meaningfully supplement them are also frequently neglected. This paper provides guidance on study designs and statistical analyses for evaluating the performance of advanced imaging in the various roles in treatment decision guidance and clinical trial conduct. Relevant methodology from the imaging literature is reviewed; gaps in the literature are addressed using related concepts from the more extensive genomic and in vitro biomarker literature.

Beyond Correlations, Sensitivities, and Specificities: Case Examples of the Evaluation of Advanced Imaging in Oncology Clinical Trials and Cancer Treatment.

Although advanced imaging is an important component of oncology clinical trials, there has not been a lot of success in advancing its use from a research perspective. One likely reason is the lack of consensus on the methodology used to study advanced imaging in trials, which results in a disconcerted research effort and produces data that are difficult to collate for use in validating the imaging components being studied. Imaging is used in cancer clinical trials for various indications, and the study design needed to evaluate the imaging in a particular indication will vary. Through case examples, this paper will discuss how advanced imaging is currently being investigated in oncology clinical trials, categorized by the potential clinical indication for the imaging tool and offer suggestions on how development should proceed to further evaluate imaging in the given indication. Available National Cancer Institute resources that can assist in this process will also be discussed.

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes.

BACKGROUND: In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. METHODS: Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff's α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman's rank correlation coefficients were used to compare HEIP and CEIP. RESULTS: Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p < 10-12. CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement. CONCLUSIONS: Quantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

RECIST 1.1 - Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group.

Radiologic imaging of disease sites plays a pivotal role in the management of patients with cancer. Response Evaluation Criteria in Solid Tumours (RECIST), introduced in 2000, and modified in 2009, has become the de facto standard for assessment of response in solid tumours in patients on clinical trials. The RECIST Working Group considers the ability of the global oncology community to implement and adopt updates to RECIST in a timely manner to be critical. Updates to RECIST must be tested, validated and implemented in a standardised, methodical manner in response to therapeutic and imaging technology advances as well as experience gained by users. This was the case with the development of RECIST 1.1, where an expanded data warehouse was developed to test and validate modifications. Similar initiatives are ongoing, testing RECIST in the evaluation of response to non-cytotoxic agents, immunotherapies, as well as in specific diseases. The RECIST Working Group has previously outlined the level of evidence considered necessary to formally and fully validate new imaging markers as an appropriate end-point for clinical trials. Achieving the optimal level of evidence desired is a difficult feat for phase III trials; this involves a meta-analysis of multiple prospective, randomised multicentre clinical trials. The rationale for modifications should also be considered; the modifications may be proposed to improve surrogacy, to provide a more mechanistic imaging technique, or be designed to improve reproducibility of the imaging biomarker. Here, we present the commonly described modifications of RECIST, each of which is associated with different levels of evidence and validation.

RECIST 1.1-Update and clarification: From the RECIST committee.

The Response Evaluation Criteria in Solid Tumours (RECIST) were developed and published in 2000, based on the original World Health Organisation guidelines first published in 1981. In 2009, revisions were made (RECIST 1.1) incorporating major changes, including a reduction in the number of lesions to be assessed, a new measurement method to classify lymph nodes as pathologic or normal, the clarification of the requirement to confirm a complete response or partial response and new methodologies for more appropriate measurement of disease progression. The purpose of this paper was to summarise the questions posed and the clarifications provided as an update to the 2009 publication.

Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.

BACKGROUND: The objective of this study was to demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage. METHODS: The authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy-proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist-measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave-1-out, cross-validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest. RESULTS: Tumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor "homogeneity" significantly improved discrimination (AUC = 0.62; P = .003) compared with chance. CONCLUSIONS: The current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748-757. © 2015 American Cancer Society.