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This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

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.

Surgical resection is the only potential curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), but unfortunately most patients recur within 5 years of surgery. This article aims to assess the practice patterns across major academic institutions and develop consensus recommendations for postoperative imaging and interpretation in patients with PDAC.The consensus recommendations for postoperative imaging surveillance following PDAC resection were developed using the Delphi method. Members of the Society of Abdominal Radiology (SAR) PDAC Disease Focused Panel (DFP) underwent three rounds of surveys followed by live webinar group discussions to develop consensus recommendations.Significant variations currently exist in the postoperative surveillance of PDAC, even among academic institutions. Differentiating common postoperative inflammatory and fibrotic changes from tumor recurrence remains a diagnostic challenge, and there is no reliable size threshold or growth rate of imaging findings that can provide differentiation. A new liver lesion or peritoneal nodule should be considered suspicious for tumor recurrence, and the imaging features should be interpreted in the appropriate clinical context (e.g., CA 19-9, clinical presentation, pathologic staging).Postoperative imaging following PDAC resection is challenging to interpret due to the presence of confounding postoperative inflammatory changes. A standardized reporting template for locoregional findings and report impression may improve communication of relaying risk of recurrence with referring providers, which merits validation in future studies.

Supplementary Table 2. Excel spreadsheet showing results of drug screen indicating sensitivity of PDA cell lines across a range of drug concentrations.

Supplementary Figures Legends

Purpose: Improved therapeutic approaches are needed for the treatment of pancreatic ductal adenocarcinoma (PDAC). As dual MEK and PI3K inhibition is presently being used in clinical trials for patients with PDAC, we sought to test the efficacy of combined targeting of these pathways in PDAC using both in vitro drug screens and genetically engineered mouse models (GEMM).Experimental Design: We performed high-throughput screening of >500 human cancer cell lines (including 46 PDAC lines), for sensitivity to 50 clinically relevant compounds, including MEK and PI3K inhibitors. We tested the top hit in the screen, the MEK1/2 inhibitor, AZD6244, for efficacy alone or in combination with the PI3K inhibitors, BKM120 or GDC-0941, in a KrasG12D-driven GEMM that recapitulates the histopathogenesis of human PDAC.Results: In vitro screens revealed that PDAC cell lines are relatively resistant to single-agent therapies. The response profile to the MEK1/2 inhibitor, AZD6244, was an outlier, showing the highest selective efficacy in PDAC. Although MEK inhibition alone was mainly cytostatic, apoptosis was induced when combined with PI3K inhibitors (BKM120 or GDC-0941). When tested in a PDAC GEMM and compared with the single agents or vehicle controls, the combination delayed tumor formation in the setting of prevention and extended survival when used to treat advanced tumors, although no durable responses were observed.Conclusions: Our studies point to important contributions of MEK and PI3K signaling to PDAC pathogenesis and suggest that dual targeting of these pathways may provide benefit in some patients with PDAC. Clin Cancer Res; 21(2); 396–404. ©2014 AACR.

Supplementary Figures 1-3. 1. MEK/PI3K signaling in human PDA 2. Prevention study 3. Treatment study

Supplementary Table 1. Excel spreadsheet showing results of drug screen indicating relative sensitivity of PDAC cell lines compared to non-PDA cell lines.

Supplementary Tables 1-3 from The Phosphoinositide 3-Kinase Regulatory Subunit p85α Can Exert Tumor Suppressor Properties through Negative Regulation of Growth Factor Signaling

Phosphoinositide 3-kinase (PI3K) plays a critical role in tumorigenesis, and the PI3K p85 regulatory subunit exerts both positive and negative effects on signaling. Expression of Pik3r1, the gene encoding p85, is decreased in human prostate, lung, ovarian, bladder, and liver cancers, consistent with the possibility that p85 has tumor suppressor properties. We tested this hypothesis by studying mice with a liver-specific deletion of the Pik3r1 gene. These mice exhibited enhanced insulin and growth factor signaling and progressive changes in hepatic pathology, leading to the development of aggressive hepatocellular carcinomas with pulmonary metastases. Liver tumors that arose exhibited markedly elevated levels of phosphatidylinositol (3,4,5)-trisphosphate, along with Akt activation and decreased PTEN expression, at both the mRNA and protein levels. Together, these results substantiate the concept that the p85 subunit of PI3K has a tumor-suppressive role in the liver and possibly other tissues. Cancer Res; 70(13); 5305–15. ©2010 AACR.

Supplementary Figures 1-6 from The Phosphoinositide 3-Kinase Regulatory Subunit p85α Can Exert Tumor Suppressor Properties through Negative Regulation of Growth Factor Signaling

Stromal cells promote extensive fibrosis in pancreatic ductal adenocarcinoma (PDAC), which is associated with poor prognosis and therapeutic resistance. We report here for the first time that loss of the RNA-binding protein human antigen R (HuR, ELAVL1) in PDAC cells leads to reprogramming of the tumor microenvironment. In multiple in vivo models, CRISPR deletion of ELAVL1 in PDAC cells resulted in a decrease of collagen deposition, accompanied by a decrease of stromal markers (i.e. podoplanin, α-smooth muscle actin, desmin). RNA-sequencing data showed that HuR plays a role in cell–cell communication. Accordingly, cytokine arrays identified that HuR regulates the secretion of signaling molecules involved in stromal activation and extracellular matrix organization [i.e. platelet-derived growth factor AA (PDGFAA) and pentraxin 3]. Ribonucleoprotein immunoprecipitation analysis and transcription inhibition studies validated PDGFA mRNA as a novel HuR target. These data suggest that tumor-intrinsic HuR supports extrinsic activation of the stroma to produce collagen and desmoplasia through regulating signaling molecules (e.g. PDGFAA). HuR-deficient PDAC in vivo tumors with an altered tumor microenvironment are more sensitive to the standard of care gemcitabine, as compared to HuR-proficient tumors. Taken together, we identified a novel role of tumor-intrinsic HuR in its ability to modify the surrounding tumor microenvironment and regulate PDGFAA.

Seiffert spirals were recently explored as an efficient way to traverse 3D k-space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire TA 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k-space, with flip angle modulation for TIn all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi-axis 2D spiral MRF sequence. Strong intraclass correlation coefficients ( 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory.In this study, Seiffert spirals were used within the MRF framework to acquire high-resolution T

Radioenhancing nanoparticles (NPs) are being evaluated in ongoing clinical trials for various cancers including head and neck, lung, esophagus, pancreas, prostate, and soft tissue sarcoma. Supported by decades of preclinical investigation and recent randomized trial data establishing clinical activity, these agents are poised to influence future multimodality treatment paradigms involving radiotherapy. Although the physical interactions between NPs and ionizing radiation are well characterized, less is known about how these agents modify the tumor microenvironment, particularly regarding tumor immunogenicity. In this review, we describe the key multidisciplinary considerations related to radiation, surgery, immunology, and pathology for designing radioenhancing NP clinical trials. This article is categorized under: Therapeutic Approaches and Drug DiscoveryNanomedicine for Oncologic Disease.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Significance These human data are consistent with reproductive control of liver size and function in women and concur with recent observations in rodents, suggesting a conserved liver biology. The question of whether this described liver biology has implications for maternal health during pregnancy or sex-specific risk for liver disease remains to be determined. However, our evidence suggestive of weaning-induced liver involution in women may lead to improved understanding of the high rates of liver metastasis observed in young postpartum breast cancer patients., During pregnancy, the rodent liver undergoes hepatocyte proliferation and increases in size, followed by weaning-induced involution via hepatocyte cell death and stromal remodeling, creating a prometastatic niche. These data suggest a mechanism for increased liver metastasis in breast cancer patients with recent childbirth. It is unknown whether the human liver changes in size and function during pregnancy and weaning. In this study, abdominal imaging was obtained in healthy women at early and late pregnancy and postwean. During pregnancy time points, glucose production and utilization and circulating bile acids were measured. Independently of weight gain, most women’s livers increased in size with pregnancy, then returned to baseline postwean. Putative roles for bile acids in liver growth and regression were observed. Together, the data support the hypothesis that the human liver is regulated by reproductive state with growth during pregnancy and volume loss postwean. These findings have implications for sex-specific liver diseases and for breast cancer outcomes.

Highlights • Radiation-induced effects visible in Gd-EOB-DTPA enhanced MRI during proton therapy. • High inter-patient variation in early MRI signal change during therapy. • Correlation of signal change with pretreatment IL-6 concentration., Background and purpose Previous MRI studies have shown a substantial decrease in normal-tissue uptake of a hepatobiliary-directed contrast agent 6–9 weeks after liver irradiation. In this prospective clinical study, we investigated whether this effect is detectable during the course of proton therapy. Material and methods Gd-EOB-DTPA enhanced MRI was performed twice during hypo-fractionated proton therapy of liver lesions in 9 patients (plus two patients with only one scan available). Dose-correlated signal changes were qualitatively scored based on difference images from the two scans. We evaluated the correlation between the MRI signal change with the planned dose map. The GTV was excluded from all analyses. In addition, were examined timing, irradiated liver volume, changes in liver function parameters as well as circulating biomarkers of inflammation. Results Strong, moderate or no dose-correlated signal changes were detected for 2, 3 and 5 patients, respectively. Qualitative scoring was consistent with the quantitative dose to signal change correlation. In an exploratory analysis, the strongest correlation was found between the qualitative scoring and pretreatment IL-6 concentration. For all patients, a clear dose-correlated signal decrease was seen in late follow-up scans. Conclusion Radiation-induced effects can be detected with Gd-EOB-DTPA enhanced MRI in a subgroup of patients within a few days after proton irradiation. The reason for the large inter-patient variations is not yet understood and will require validation in larger studies.

Background In order to establish the workflows required to implement a real-time process involving multi-omic analysis of patient samples to support precision-guided therapeutic intervention, a tissue acquisition and analysis trial was implemented. This report describes our findings to date, including the frequency with which mutational testing led to precision-guided therapy and outcome for those patients. Methods Eligible patients presenting to Oregon Health and Science University Knight Cancer Institute were enrolled on the study. Patients with biopsy proven metastatic or locally advanced unresectable prostate cancer, breast cancer, pancreatic adenocarcinoma, or refractory acute myelogenous leukemia receiving standard of care therapy were eligible. Metastatic site biopsies were collected and analyzed using the Knight Diagnostic Lab GeneTrails comprehensive solid tumor panel (124 genes). CLIA certified genomic information was made available to the treating physician. Results Between 1/26/2017 and 5/30/2018, 38 patients were enrolled, with 28 successfully undergoing biopsy. Of these, 25 samples yielded sufficient tumor for analysis. The median biopsy cellularity and number of cores collected were 70% (15–90%) and 5 (2–20), respectively. No procedure-related complications occurred. GeneTrails analysis revealed that 22 of 25 (88%) tumor samples harbored at least one potentially actionable mutation, and 18 (72%) samples harbored 2 or more potentially actionable mutations. The most common genetic alterations identified involved: DNA damage repair genes, cell cycle regulating genes, PIK3CA/Akt/mTOR pathway, and FGF gene family. To date, CLIA certified genomic results were used by treating physicians for precision-guided therapy in 5 (23%) patients. Conclusion We report the feasibility of real-time tissue acquisition and analysis to support a successful translational oncology platform. The workflow will provide the foundation to improve access and accrual to biomarker driven precision oncology trials. Electronic supplementary material The online version of this article (10.1186/s12967-018-1733-y) contains supplementary material, which is available to authorized users.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

The practice of medicine is becoming more of a science and less of an art, and imaging science needs to keep pace in order to remain relevant. Treating physicians need and expect objective, reproducible, ideally quantitative, results from diagnostic tests, but the medical community continues to be challenged by the lack of objective, qualified imaging biomarkers useful for disease prevention, detection, and treatment. Recent advances in imaging technology present opportunities to meet these needs, but the deployment of qualified quantitative imaging biomarkers (QIBs) lags behind the technological advances. It is widely acknowledged that the primary factor limiting large-scale deployment of imaging biomarkers is the lack of standardization on how to implement them across each of the stakeholders. The current differentiation in imaging vendor products, as well as the independent activities of clinicians, individual biopharmaceutical companies, and related clinical research organizations, stands in the way of the greater opportunity that would exist if these efforts were drawn together. The formation of the Quantitative Imaging Biomarkers Alliance (QIBA) from the Radiological Society of North America (RSNA) provides a productive structure for all participants in the imaging chain to integrate activities and allow stakeholders to collaborate on the methodology to obtain QIBs. The QIBA process provides a path in the innovative development of new, promising, and objective imaging biomarkers (which benefits the imaging device industry), and innovative therapies facilitated by cost-effective, qualified biomarkers (which benefits care providers, the pharmaceutical industry, and other healthcare product providers). This chapter provides a history of QIBA from its inception, ending in a present state and challenges moving forward.

SummaryMechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancer’s genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Due to complexity of advanced epithelial cancers, it is necessary to implement patient specific combination therapies if we are to markedly improve patient outcomes. However, our ability to select and implement patient specific combination therapies based on dynamic molecular changes in the tumor and tumor ecosystem in response to therapy remains extremely limited. In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). Although PARP inhibition was consistently observed in all patients, deep molecular analysis of the tumor and its ecosystem revealed insights into potentially effective therapeutic PARPi combinations. In a BRCA-mutant basal breast cancer exceptional long-term survivor, we noted striking PARPi-induced tumor destruction accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor (LAR) rapid progressor in response to PARPi likely due to indifference to the effects of PARP inhibition. In this rapid progressor, there was minimal evidence of immune activation or protein network rewiring in response to PARPi, despite PARP being inhibited, and no clinical benefit was noted for this participant. Together, deep real-time analysis of longitudinal biopsies identified a suite of PARPi-induced emergent changes including immune activation, DNA damage checkpoint activation, apoptosis and signaling pathways including RTK, PI3K-AKT and RAS-MAPK, that could be used to select patient specific combination therapies, based on tumor and immune state changes that are likely to benefit specific patients.HighlightsLongitudinal analysis of serial tumor samples in real-time identifies adaptive mechanisms of resistance to PARPi therapies.Deep molecular analysis of the tumor reveals insights into potentially effective therapeutic PARPi combinations.Extensive protein network rewiring, microenvironment and immune state changes are assessable factors for patient specific combination therapies.

T1ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity, liver fibrosis, tumors, cardiac infarction, and Alzheimer's disease. T1 , T2 , and T1ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T1 and T2 relaxation time constants. In this study, T1ρ relaxation is added to the MRF framework using spin lock preparations. An MRF sequence involving an RF-spoiled sequence with TR , flip angle, T1ρ , and T2 preparation variation is described. The sequence is then calibrated against conventional T1 , T2 , and T1ρ relaxation mapping techniques in agar phantoms and the abdomens of four healthy volunteers. Strong intraclass correlation coefficients (ICC > 0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC > 0.8). The highest ICC correlation values were seen in T1 , followed by T1ρ and then T2 . In this study, T1ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T1 and T2 relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

PET/MR imaging has the potential to markedly alter pancreatic care in both the malignant, and premalignant states with the ability to perform robust, high-resolution, quantitative molecular imaging. The ability of PET/MR imaging to monitor disease processes, potentially correct for motion in the upper abdomen, and provide novel biomarkers that may be a combination of MR imaging and PET biomarkers, offers a unique, precise interrogation of the pancreatic milieu going forward.

Many of the current approaches to personalized medicine rely on sequencing DNA to identify actionable mutations. However, growing evidence suggests that a multi-omic approach to more broadly assess biology is needed to improve patient outcomes. We have implemented a workflow for tissue acquisition, multi-omic clinical testing, and correlated computational biology analysis, within the Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART) Program (Mitri et al, J Transl Med 2018). We report biopsy metrics, CLIA analytics utilized, the operation of a multi-omics tumor board, and clinical outcomes in metastatic breast cancer patients. A detailed clinical history of each patient was obtained, including demographics, tumor type, treatment and response to prior therapies, imaging, and blood tumor biomarkers. A comprehensive set of clinical assays was performed on newly obtained tumor biopsies, including immunohistochemistry (ER, PR, HER2, AR, BCL-2, and PD-L1), a targeted next-generation sequencing panel covering 225 genes (GeneTrails® Comprehensive Solid Tumor Panel), whole exome sequencing (Tempus xE), whole transcriptomic sequencing (Illumina TruSeq RNA exome), and a multiplex protein analysis of 22 key cancer proteins and phosphoproteins on the Nanostring platform (NanoString Vantage 3D™ Solid Tumor Panel). The integrated clinical and analytical information was made available to the multidisciplinary SMMART Clinical Tumor Board that provided treatment recommendations; the final treatment plan was at the discretion of the treating physician. Between 1/1/2017-1/1/2020, 53 breast cancer patients were consented. Seven screen-failed due to a lack of sites amenable to biopsy and 8 were actively co-consented to other clinical trials. The remaining 38 patients are included in this preliminary report. A total of 63 biopsies were collected from lymph node, liver, bone, soft tissue, lung, skin, breast, and brain. Serial biopsies (≥2) were obtained for 15 patients. Analytics were generated in 93.7% of biopsies. Tumor boards were held for 15 patients (17 total sessions). The experience and information gathered thus far have yielded the following unique cases: (1) single patient analysis of omics, imaging, and response over 42 months, (2) identification of an ERBB3 mutation with downstream pathway activation that responded to HER2-targeted therapy, and (3) clinically significant variation in hormonal and HER2 receptor status over time. We will provide an analysis across the 38 patients of treatment outcomes, analytics information content, biological changes observed through serial biopsies, and tumor board interventions. We demonstrate the feasibility of implementing a deep, real-time analytics platform for metastatic breast cancer patients that can provide new insight into therapeutic opportunities. The observed clinical responses support further use and investigation of this approach. Citation Format: Ben L. Kong, Brett E. Johnson, Jamie M. Keck, Souraya Mitri, Patrick Leyshock, Jayne M. Stommel, Kiara Siex, Marlana Klinger, Christina L. Zheng, Rochelle Williams-Belizaire, Shannon McWeeney, Jeremy Goecks, Annette Kolodzie, Alexander R. Guimaraes, George V. Thomas, Christopher L. Corless, Zahi I. Mitri, Joe W. Gray, Gordon B. Mills, Raymond C. Bergan. SMMART Program: A multi-omics tumor board with a focus on breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB010.

Purpose Metastatic castration-resistant prostate cancer (CRPC) is the lethal form of the disease. Many groups have performed mutational or immunohistochemistry (IHC) testing in metastatic CRPC to identify treatment targets. However, the frequency with which mutational or IHC data have an impact on clinical decision making and the outcomes of molecularly guided therapy in CRPC are largely unknown. We report our institution’s experience with mutational and IHC testing in patients with metastatic CRPC and its impact on clinical decision making and patient outcomes. Methods Between 2012 and 2015, 59 patients with CRPC underwent metastatic tissue biopsies and were genotyped with a 37–cancer gene panel in a Clinical Laboratory Improvement Amendments–certified laboratory. PTEN expression by IHC testing was also measured in 35 of these samples. A retrospective chart review was performed to determine whether the genomic information was acted upon and the outcome of patients whose treatment was guided by molecular testing. Results Forty-six of 59 patients with CRPC (78.0%) had biopsies with adequate tumor for mutational testing. Thirty-one of 46 subjects (67.4%) had mutations identified by sequencing. Of the 35 patients with CRPC whose biopsies were evaluated for PTEN expression by IHC testing, 13 had PTEN loss. Two patients had treatment on the basis of molecular testing, and one of these subjects had greater tumor control with molecularly guided therapy than his immediate prior therapy. Conclusion Targeted sequencing and IHC can identify clinically informative molecular abnormalities in CRPC. Despite this, a small minority of patients in our series underwent therapies guided by mutational or IHC testing. Actionability of abnormalities identified in metastatic CRPC may be improved with access to clinical trials, insurance approval for unapproved uses of existing anticancer drugs, and larger gene sequencing panels that include more frequently mutated genes.

Contrast-enhanced magnetic resonance imaging is a commonly used diagnostic tool. Compared with standard gadolinium-based contrast agents, ferumoxytol (Feraheme, AMAG Pharmaceuticals, Waltham, MA), used as an alternative contrast medium, is feasible in patients with impaired renal function. Other attractive imaging features of i.v. ferumoxytol include a prolonged blood pool phaseand delayed intracellular uptake. With its unique pharmacologic, metabolic, and imaging properties, ferumoxytol may play a crucial role in future magnetic resonance imaging of the central nervous system, various organs outside the central nervous system, and the cardiovascular system. Preclinical and clinical studies have demonstrated the overall safety and effectiveness of this novel contrast agent, with rarely occurring anaphylactoid reactions. The purpose of this review is to describe the general and organ-specific properties of ferumoxytol, as well as the advantages and potential pitfalls associated with its use in magnetic resonance imaging. To more fully demonstrate the applications of ferumoxytol throughout the body, an imaging atlas was created and is available online as supplementary material.

T(1)ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity(1–4), liver fibrosis(5), tumors(6), cardiac infarction(7), and Alzheimer’s disease(8). T(1), T(2), and T(1)ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T(1) and T(2) relaxation time constants(9). In this study, T(1)ρ relaxation is added to the MRF framework using spin lock preparations (SLPs). A MR fingerprinting sequence involving an RF-spoiled sequence with TR, FA, T(1)ρ, and T(2) preparation variation is described. The sequence is then calibrated against conventional T(1), T(2), and T(1)ρ relaxation mapping techniques in agar phantoms and the abdomen of four healthy volunteers. Strong intra-class correlation coefficients (ICC>0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC>0.8). The highest ICC correlation values were seen in T(1), followed by T(1)ρ and then T(2). In this study, T(1)ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T(1) and T(2) relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.

PURPOSE: Losartan, an angiotensin II receptor blocker, can reduce desmoplasia and enhance drug delivery and efficacy through improving interstitial transport and vascular perfusion in pancreatic ductal adenocarcinoma (PDAC) models in mice. The purpose of this study was to determine whether magnetic resonance imaging (MRI) of magnetic iron oxide nanoparticles (MNPs) and micro–positron emission tomography (PET) measurements could respectively detect improvements in tumor vascular parameters and drug uptake in orthotopic PDAC in mice treated with losartan. METHOD AND MATERIALS: All experiments were approved by the local Institutional Animal Care and Use Committee. FVB mice with orthotopic PDAC were treated daily with an i.p. injection of losartan (70 mg/kg) or saline (control vehicle) for 5 days. In order to calculate the fractional blood volume, vessel size index, and vessel density index, MRI was performed at 4.7 T following the injection of 3 mg/kg iron ferumoxytol (i.v.). Dynamic PET images were also acquired for 60 minutes using an 18F-5FU tracer dose of 200 μCi and analyzed for time activity curves normalized to muscle. Statistical analyses compared both cohorts using an unpaired two-tailed t test. RESULTS: In comparison to the control treatment, the losartan administration significantly increased the fractional blood volume (mean ± SEM) [12.1 ± 1.7 (n = 19) vs 6.7 ± 1.1 (n = 20); P

The purpose of the study was to assess the diagnostic performance of qualitative and quantitative diffusion-weighted imaging (DWI) in differentiating benign from malignant ovarian and uterine masses. Institutional review board approval was obtained for this HIPAA-compliant retrospective study, with waiver of informed consent. DWI MRIs of 222 women acquired over 1.5 years were evaluated. Reference standard was pathology or follow-up imaging. For qualitative assessment, two radiologists independently reviewed DWI and apparent diffusion coefficient (ADC) images for diffusion restriction. Differences were resolved by consensus. For quantitative assessment, a single reader measured ADC values. Readers were blinded to the reference standard. 222 lesions, 121 ovarian (99 benign and 22 malignant) and 101 uterine (54 benign and 47 malignant), were included. Final diagnosis was established with pathology in 129 (58%) or with imaging follow-up in 93 (42%). Mean (range) follow-up interval was 27 (13–48) months. Qualitative assessment yielded sensitivity (ratio, 95% CI), specificity, PPV and NPV of 100% (22/22, 85–100), 68% (68/99, 58–76), 41% (22/54, 27–54), and 100% (68/68, 94–100) for ovarian and 94% (44/47, 83–98), 91% (49/54, 80–96), 90% (44/49, 78–95) and 94% (49/52, 84–98) for uterine malignancies. ADC (mean ± SD) between benign ovarian [(1.11 ± 0.76) × 10−3 mm2/s] vs. malignant [(0.71 ± 0.26) × 10−3 mm2/s] lesions was significantly different (p

To review the radiological appearances of corpus luteum cysts and their imaging mimics. Corpus luteum cysts are normal post-ovulatory structures seen in the ovaries through the second half of the menstrual cycle and the first trimester of pregnancy. The typical appearance, across all modalities, is of a 1- to 3-cm cyst with a thick crenulated vascularized wall. Occasionally, similar imaging findings may be seen with endometrioma, ectopic pregnancy, tuboovarian abscess, red degeneration of a fibroid, and ovarian neoplasia. In most cases, imaging findings are distinctive and allow for a confident and accurate diagnosis that provides reassurance for patients and referring physicians and avoids costly unnecessary follow-up.

Purpose To evaluate the incremental effect of perfusion imaging on biopsy target identification at endorectal multiparametric prostate magnetic resonance imaging (MRI). Materials and methods We retrospectively 52 patients who underwent endorectal multiparametric prostate MRI for suspected or untreated prostate cancer. Two readers independently identified biopsy targets without and with perfusion images. Results Reader 1 identified 36 targets without and 39 targets with perfusion imaging ( P >.05). The corresponding numbers for reader 2 were 38 and 38, respectively ( P =.5). Conclusion Perfusion imaging does not significantly increase the number of biopsy targets identified at endorectal multiparametric prostate MRI.

BACKGROUND The grading of liver fibrosis relies on liver biopsy. Imaging techniques, including elastography and relaxometric, techniques have had varying success in diagnosing moderate fibrosis. The goal of this study was to determine if there is a relationship between the T2-relaxation time of hepatic parenchyma and the histologic grade of liver fibrosis in patients with hepatitis C undergoing both routine, liver MRI and liver biopsy, and to validate our methodology with phantoms and in a rat model of liver fibrosis. METHODS This study is composed of three parts: (I) 123 patients who underwent both routine, clinical liver MRI and biopsy within a 6-month period, between July 1999 and January 2010 were enrolled in a retrospective study. MR imaging was performed at 1.5 T using dual-echo turbo-spin echo equivalent pulse sequence. T2 relaxation time of liver parenchyma in patients was calculated by mono-exponential fit of a region of interest (ROI) within the right lobe correlating to histopathologic grading (Ishak 0-6) and routine serum liver inflammation [aspartate aminotransferase (AST) and alanine aminotransferase (ALT)]. Statistical comparison was performed using ordinary logistic and ordinal logistic regression and ANOVA comparing T2 to Ishak fibrosis without and using AST and ALT as covariates; (II) a phantom was prepared using serial dilutions of dextran coated magnetic iron oxide nanoparticles. T2 weighed imaging was performed by comparing a dual echo fast spin echo sequence to a Carr-Purcell-Meigboom-Gill (CPMG) multi-echo sequence at 1.5 T. Statistical comparison was performed using a paired t-test; (III) male Wistar rats receiving weekly intraperitoneal injections of phosphate buffer solution (PBS) control (n=4 rats); diethylnitrosamine (DEN) for either 5 (n=5 rats) or 8 weeks (n=4 rats) were MR imaged on a Bruker Pharmascan 4.7 T magnet with a home-built bird-cage coil. T2 was quantified by using a mono-exponential fitting algorithm on multi-slice multi echo T2 weighted data. Statistical comparison was performed using ANOVA. RESULTS (I) Histopathologic evaluation of both rat and human livers demonstrated no evidence of steatosis or hemochromatosis There was a monotonic increase in mean T2 value with increasing degree of fibrosis (control 65.4±2.9 ms, n=6 patients); mild (Ishak 1-2) 66.7±1.9 ms (n=30); moderate (Ishak 3-4) 71.6±1.7 ms (n=26); severe (Ishak 5-6) 72.4±1.4 ms (n=61); with relatively low standard error (~2.9 ms). There was a statistically significant difference between degrees of mild (Ishak 4) (P=0.03) based on logistic regression of T2 and Ishak, which became insignificant (P=0.07) when using inflammatory markers as covariates. Expanding on this model using ordinal logistic regression, there was significance amongst all 4 groups comparing T2 to Ishak (P=0.01), with significance using inflammation as a covariate (P=0.03) and approaching statistical significance amongst all groups by ANOVA (P=0.07); (II) there was a monotonic increase in T2 and statistical significance (ANOVA P

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.