Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs.

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

Altered exocrine function can drive adipose wasting in early pancreatic cancer.

Malignancy is accompanied by changes in the metabolism of both cells and the organism1,2. Pancreatic ductal adenocarcinoma (PDAC) is associated with wasting of peripheral tissues, a metabolic syndrome that lowers quality of life and has been proposed to decrease survival of patients with cancer3,4. Tissue wasting is a multifactorial disease and targeting specific circulating factors to reverse this syndrome has been mostly ineffective in the clinic5,6. Here we show that loss of both adipose and muscle tissue occurs early in the development of pancreatic cancer. Using mouse models of PDAC, we show that tumour growth in the pancreas but not in other sites leads to adipose tissue wasting, suggesting that tumour growth within the pancreatic environment contributes to this wasting phenotype. We find that decreased exocrine pancreatic function is a driver of adipose tissue loss and that replacement of pancreatic enzymes attenuates PDAC-associated wasting of peripheral tissues. Paradoxically, reversal of adipose tissue loss impairs survival in mice with PDAC. When analysing patients with PDAC, we find that depletion of adipose and skeletal muscle tissues at the time of diagnosis is common, but is not associated with worse survival. Taken together, these results provide an explanation for wasting of adipose tissue in early PDAC and suggest that early loss of peripheral tissue associated with pancreatic cancer may not impair survival.

Cancer Diagnoses After Recent Weight Loss.

Importance: Weight loss is common in primary care. Among individuals with recent weight loss, the rates of cancer during the subsequent 12 months are unclear compared with those without recent weight loss. Objective: To determine the rates of subsequent cancer diagnoses over 12 months among health professionals with weight loss during the prior 2 years compared with those without recent weight loss. Design, Setting, and Participants: Prospective cohort analysis of females aged 40 years or older from the Nurses' Health Study who were followed up from June 1978 until June 30, 2016, and males aged 40 years or older from the Health Professionals Follow-Up Study who were followed up from January 1988 until January 31, 2016. Exposure: Recent weight change was calculated from the participant weights that were reported biennially. The intentionality of weight loss was categorized as high if both physical activity and diet quality increased, medium if only 1 increased, and low if neither increased. Main Outcome and Measures: Rates of cancer diagnosis during the 12 months after weight loss. Results: Among 157 474 participants (median age, 62 years [IQR, 54-70 years]; 111 912 were female [71.1%]; there were 2631 participants [1.7%] who self-identified as Asian, Native American, or Native Hawaiian; 2678 Black participants [1.7%]; and 149 903 White participants [95.2%]) and during 1.64 million person-years of follow-up, 15 809 incident cancer cases were identified (incident rate, 964 cases/100 000 person-years). During the 12 months after reported weight change, there were 1362 cancer cases/100 000 person-years among all participants with recent weight loss of greater than 10.0% of body weight compared with 869 cancer cases/100 000 person-years among those without recent weight loss (between-group difference, 493 cases/100 000 person-years [95% CI, 391-594 cases/100 000 person-years]; P < .001). Among participants categorized with low intentionality for weight loss, there were 2687 cancer cases/100 000 person-years for those with weight loss of greater than 10.0% of body weight compared with 1220 cancer cases/100 000 person-years for those without recent weight loss (between-group difference, 1467 cases/100 000 person-years [95% CI, 799-2135 cases/100 000 person-years]; P < .001). Cancer of the upper gastrointestinal tract (cancer of the esophagus, stomach, liver, biliary tract, or pancreas) was particularly common among participants with recent weight loss; there were 173 cancer cases/100 000 person-years for those with weight loss of greater than 10.0% of body weight compared with 36 cancer cases/100 000 person-years for those without recent weight loss (between-group difference, 137 cases/100 000 person-years [95% CI, 101-172 cases/100 000 person-years]; P < .001). Conclusions and Relevance: Health professionals with weight loss within the prior 2 years had a significantly higher risk of cancer during the subsequent 12 months compared with those without recent weight loss. Cancer of the upper gastrointestinal tract was particularly common among participants with recent weight loss compared with those without recent weight loss.

Spatial signatures identify immune escape via PD-1 as a defining feature of T-cell/histiocyte-rich large B-cell lymphoma.

T-cell/histiocyte-rich large B-cell lymphoma (TCRLBCL) is an aggressive variant of diffuse large B-cell lymphoma (DLBCL) characterized by rare malignant B cells within a robust but ineffective immune cell infiltrate. The mechanistic basis of immune escape in TCRLBCL is poorly defined and not targeted therapeutically. We performed a genetic and quantitative spatial analysis of the PD-1/PD-L1 pathway in a multi-institutional cohort of TCRLBCLs and found that malignant B cells harbored PD-L1/PD-L2 copy gain or amplification in 64% of cases, which was associated with increased PD-L1 expression (P = .0111). By directed and unsupervised spatial analyses of multiparametric cell phenotypic data within the tumor microenvironment, we found that TCRLBCL is characterized by tumor-immune "neighborhoods" in which malignant B cells are surrounded by exceptionally high numbers of PD-L1-expressing TAMs and PD-1+ T cells. Furthermore, unbiased clustering of spatially resolved immune signatures distinguished TCRLBCL from related subtypes of B-cell lymphoma, including classic Hodgkin lymphoma (cHL) and DLBCL-NOS. Finally, we observed clinical responses to PD-1 blockade in 3 of 5 patients with relapsed/refractory TCRLBCL who were enrolled in clinical trials for refractory hematologic malignancies (NCT03316573; NCT01953692), including 2 complete responses and 1 partial response. Taken together, these data implicate PD-1 signaling as an immune escape pathway in TCRLBCL and also support the potential utility of spatially resolved immune signatures to aid the diagnostic classification and immunotherapeutic prioritization of diverse tumor types.

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival. Materials and Methods After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using χ2 tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI). Results External validation showed excellent correlation (R = 0.99) and equivalency (P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables (P < .001 except for subcutaneous fat area vs age [P = .003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used (P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival (P = .04) in combined models that included BMI. Conclusion Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lead to significant time delays. This HIPAA-compliant, institutional review board-approved, retrospective clinical study required identification and retrieval of all outpatient and emergency patients undergoing abdominal and pelvic computed tomography (CT) at three affiliated hospitals in the year 2012. If a patient had multiple abdominal CT exams, the first exam was selected for retrieval (n=23,186). Our experience in attempting to retrieve 23,186 abdominal CT exams yielded 22,852 valid CT abdomen/pelvis exams and identified four major categories of challenges when retrieving large datasets: cohort selection and processing, retrieving DICOM exam files from PACS, data storage, and non-recoverable failures. The retrieval took 3 months of project time and at minimum 300 person-hours of time between the primary investigator (a radiologist), a data scientist, and a software engineer. Exam selection and retrieval may take significantly longer than planned. We share our experience so that other investigators can anticipate and plan for these challenges. We also hope to help institutions better understand the demands that may be placed on their infrastructure by large-scale medical imaging machine learning projects.

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences.

Accuracy and feasibility of estimated tumour volumetry in primary gastric gastrointestinal stromal tumours: validation using semiautomated technique in 127 patients.

OBJECTIVES: To validate estimated tumour volumetry in primary gastric gastrointestinal stromal tumours (GISTs) using semiautomated volumetry. METHODS: In this IRB-approved retrospective study, we measured the three longest diameters in x, y, z axes on CTs of primary gastric GISTs in 127 consecutive patients (52 women, 75 men, mean age 61 years) at our institute between 2000 and 2013. Segmented volumes (Vsegmented) were obtained using commercial software by two radiologists. Estimate volumes (V1-V6) were obtained using formulae for spheres and ellipsoids. Intra- and interobserver agreement of Vsegmented and agreement of V1-6 with Vsegmented were analysed with concordance correlation coefficients (CCC) and Bland-Altman plots. RESULTS: Median Vsegmented and V1-V6 were 75.9, 124.9, 111.6, 94.0, 94.4, 61.7 and 80.3 cm(3), respectively. There was strong intra- and interobserver agreement for Vsegmented. Agreement with Vsegmented was highest for V6 (scalene ellipsoid, x ≠ y ≠ z), with CCC of 0.96 [95 % CI 0.95-0.97]. Mean relative difference was smallest for V6 (0.6 %), while it was -19.1 % for V5, +14.5 % for V4, +17.9 % for V3, +32.6 % for V2 and +47 % for V1. CONCLUSION: Ellipsoidal approximations of volume using three measured axes may be used to closely estimate Vsegmented when semiautomated techniques are unavailable. KEY POINTS: Estimation of tumour volume in primary GIST using mathematical formulae is feasible. Gastric GISTs are rarely spherical. Segmented volumes are highly concordant with three axis-based scalene ellipsoid volumes. Ellipsoid volume can be used as an alternative for automated tumour volumetry.

Radiation Therapy for Soft-Tissue Sarcomas: A Primer for Radiologists.

Radiation therapy (RT) plays an important role in multimodality therapy for soft-tissue sarcomas (STS). RT treatment paradigms have evolved significantly in recent years, and many different complex RT modalities are commonly used in STS. These include external-beam RT, intensity-modulated RT, stereotactic body RT, and brachytherapy. Imaging is essential throughout the treatment process. Plain radiographs, computed tomography (CT), magnetic resonance imaging, ultrasonography, and positron emission tomography/CT all play potential roles in the management of STS. Before RT, high-quality imaging is needed to direct management decisions, both by global tumor staging and detailed assessment of the extent of local disease. At the time of RT, precise planning imaging is required to delineate tumor volumes, including gross tumor volume, clinical target volume, and planning target volume, which are used to direct therapy. In addition, imaging at the time of RT must outline the location of adjacent vital organs, to optimize treatment efficacy and minimize toxicity. After RT, imaging is needed to assess the patient for tumor response to therapy. In addition, imaging at regular intervals is often required to monitor for recurrence of disease and potential complications of therapy. The purpose of this review is to familiarize radiologists with the indications for RT in STS, common therapeutic modalities used, roles of imaging throughout the treatment process, and complications of therapy.

Basal cell carcinoma: a comprehensive review for the radiologist.

OBJECTIVE. Basal cell carcinoma (BCC) is the most common malignancy in the United States. The purpose of this article is to provide a comprehensive description of the clinicopathologic features, diagnostic workup, staging, treatment, and follow-up of BCC. CONCLUSION. Radiology plays an important role in the evaluation and staging of locally advanced and metastatic BCC. MRI is the modality of choice for assessing perineural disease and is equivalent or superior to CT for evaluating bony involvement. CT and PET/CT are used to evaluate metastatic disease.

Beyond the vascular endothelial growth factor axis: update on role of imaging in nonantiangiogenic molecular targeted therapies in oncology.

OBJECTIVE: This article provides a comprehensive review of molecular targeted therapies that do not act directly through vascular endothelial growth factor pathways, highlighting the role of imaging in assessment of treatment response and drug toxicities. CONCLUSION: A substantial number of molecular targeted therapies act on nonantiangiogenic pathways. Familiarity with these drugs, their personalized tumor response criteria, and class- and drug-specific toxicities will help radiologists to be an integral part of the multi-disciplinary oncology team.

Imaging of Fluid in Cancer Patients Treated With Systemic Therapy: Chemotherapy, Molecular Targeted Therapy, and Hematopoietic Stem Cell Transplantation.

OBJECTIVE: The purpose of this article is to provide a comprehensive review of the imaging features of various systemic treatment-related causes of fluid accumulation in cancer patients. CONCLUSION: Systemic treatment-related fluid accumulation can occur with chemotherapy, molecular targeted therapy, or hematopoietic stem cell transplantation. Imaging findings such as new ascites, pleural and pericardial effusions, and subcutaneous edema should be interpreted with caution on restaging studies.

Hormonal therapy in oncology: a primer for the radiologist.

OBJECTIVE: The purpose of this article is to provide a comprehensive imaging review of the common hormonal therapies used in oncology and the side effects associated with them. CONCLUSION: Commonly used hormones in oncology include corticosteroids, somatostatin analogues, progestins, gonadotropin-releasing hormone agonists and antagonists, antiandrogens, aromatase inhibitors, and selective estrogen receptor modulators. Familiarity with these hormones and their side effects can help radiologists to be vigilant for the side effects and complications of these agents.

Myxoid soft-tissue neoplasms: comprehensive update of the taxonomy and MRI features.

OBJECTIVE. The purpose of this article is to review the classification, clinical presentation, and histopathologic and MRI features of myxoid soft-tissue neoplasms. CONCLUSION. MRI is the modality of choice for characterization of myxoid soft-tissue tumors. A combination of imaging features (including certain characteristic signs), clinical features, and patient demographics can help the radiologist in coming to a specific diagnosis or in narrowing down the differential diagnoses.

Anorectal Cancer: Critical Anatomic and Staging Distinctions That Affect Use of Radiation Therapy.

Although rectal and anal cancers are anatomically close, they are distinct entities with different histologic features, risk factors, staging systems, and treatment pathways. Imaging is at the core of initial clinical staging of these cancers and most commonly includes magnetic resonance imaging for local-regional staging and computed tomography for evaluation of metastatic disease. The details of the primary tumor and involvement of regional lymph nodes are crucial in determining if and how radiation therapy should be used in treatment of these cancers. Unfortunately, available imaging modalities have been shown to have imperfect accuracy for identification of nodal metastases and imaging features other than size. Staging of nonmetastatic rectal cancers is dependent on the depth of invasion (T stage) and the number of involved regional lymph nodes (N stage). Staging of nonmetastatic anal cancers is determined according to the size of the primary mass and the combination of regional nodal sites involved; the number of positive nodes at each site is not a consideration for staging. Patients with T3 rectal tumors and/or involvement of perirectal, mesenteric, and internal iliac lymph nodes receive radiation therapy. Almost all anal cancers warrant use of radiation therapy, but the extent and dose of the radiation fields is altered on the basis of both the size of the primary lesion and the presence and extent of nodal involvement. The radiologist must recognize and report these critical anatomic and staging distinctions, which affect use of radiation therapy in patients with anal and rectal cancers.

Anti-VEGF molecular targeted therapies in common solid malignancies: comprehensive update for radiologists.

Angiogenesis is an essential component of the growth and dissemination of solid malignancies and is mediated by several proangiogenic factors. The most widely studied proangiogenic factor is vascular endothelial growth factor (VEGF). A major class of molecular targeted therapies (MTTs) inhibit the VEGF axis and are referred to as antiangiogenic MTTs. There are two main types of anti-VEGF MTTs: drugs targeting circulating VEGF and drugs interfering with the activity of the VEGF receptors. The cancers against which antiangiogenic MTTs have had the greatest effect are gliomas, non-small cell lung cancer, colorectal cancer, hepatocellular carcinoma, renal cell carcinoma, and gastrointestinal stromal tumor. These cancers respond to antiangiogenic MTTs in a different way than they respond to conventional chemotherapy. Instead of the traditional Response Evaluation Criteria in Solid Tumors (RECIST), each of these cancers therefore requires its own individualized treatment response criteria (TRC). Examples of individualized TRC include the Response Assessment in Neuro-oncology (RANO) criteria for gliomas, modified RECIST for hepatocellular carcinoma, and Morphology, Attenuation, Size, and Structure (MASS) criteria for renal cell carcinoma. Furthermore, antiangiogenic MTTs have a unique spectrum of class-specific and drug-specific toxic effects, some of which can be detected at imaging. Increasing use of antiangiogenic MTTs in clinical practice necessitates that radiologists be aware of these drugs, their response patterns, and TRC as well as their toxic effect profiles.