The growing volume of clinical imaging and the emergence of artificial intelligence technologies present a unique opportunity to extract additional value from existing imaging data that would otherwise go unused, providing patients with health benefits beyond the original imaging purpose through value-added opportunistic screening. By targeting clinically significant diseases and major public health concerns, opportunistic screening can enhance existing risk assessment, prevention, and treatment paradigms, expanding radiology's reach and impact for individual patients and at the population level.

Cardiovascular disease is the leading cause of mortality and morbidity worldwide. Coronary artery calcium score improves risk stratification and helps determine eligibility for statin therapy. With the introduction of the 2021 American College of Cardiology and American Heart Association chest pain guideline, coronary computed tomographic angiography (CCTA) is being performed more frequently as an initial method of imaging in symptomatic individuals. As a noninvasive imaging modality, CCTA can detect and classify high-risk noncalcified plaques and identify both obstructive and nonobstructive diseases even in asymptomatic individuals, which has the potential to personalize preventive therapies and help prevent future cardiovascular events.

Endometriosis affects 1 in 10 women of reproductive age and is often diagnosed after years of symptom onset. Although population-wide screening is not recommended in asymptomatic women, targeted imaging-based assessment in symptomatic high-risk groups offers a feasible pathway for early detection. This article summarizes the evidence supporting imaging-led diagnostic strategies for early disease detection. The concept of augmented pelvic ultrasound and the role of MRI for disease detection are described. MRI for presurgical disease mapping and the added value of structured reporting are explained. The pillars of a successful screening program in screening eligible diseases and how these strategies can be extrapolated for endometriosis diagnosis in an enriched population are proposed.

Hereditary cancer syndromes are characterized by increased susceptibility to early-onset development of multifocal, organ-specific, and pathogenetically unique tumors. Commonly inherited as autosomal dominant disorders, these syndromes are associated with germline mutations of single genes that function as tumor suppressor genes. Recent advances in genetics and pathology have thrown fresh light on the molecular biology, tumor pathways, and behavior of distinctive tumors that occur in these syndromes. Comprehensive investigations of these rare syndromes have led to a better understanding of the pathogenesis and management of the more common sporadic neoplasms, including deciphering of driver mutations, tumor pathways, natural history, and prognosis.

Prostate cancer screening has entered a new era with the integration of MR imaging and artificial intelligence (AI) into diagnostic workflows. This paradigm shift allows for the more accurate identification of clinically significant prostate cancer while reducing the overdiagnosis of low-grade disease. AI-driven image reconstruction and lesion detection platforms are improving diagnostic accuracy and access. This article synthesizes recent advances in MR imaging-based prostate cancer screening, focusing on the evidence for its utility, risk-adjusted screening strategies, emerging AI applications, and future directions aimed at increasing precision, efficiency, and equity in prostate cancer care.

Ovarian cancer is the second-most common gynecologic malignancy and one of the leading causes of cancer-related deaths in women. However, its incidence remains low, accounting for approximately 1% of all types of cancers. Ovarian cancer encompasses a heterogeneous group of tumors classified based on distinctive histopathologic and molecular features. Epithelial ovarian tumors are the most common type, accounting for approximately 90% of ovarian cancers, and can be further divided into type I and type II cancers. Type II epithelial ovarian cancers are the most common and most aggressive, with high-grade serous carcinoma being the most common subtype.

Screening for early-onset colorectal cancer is a growing public health concern, driven by a rising incidence in younger adults. As trends shift, screening guidelines continue to evolve. In this article, we examine recent epidemiologic patterns and potential causes of this shift. We also explore recent updates on current screening modalities, both structural and nonstructural, highlighting their efficancy, advantages, limitations, and cost-effectiveness to inform future clinical and policy decisions, with a focus on CT colonogrpahy. Additionally, we discuss the evolving complementary role of artificial intelligence in enhancing the accuracy of screening tests.

Gallbladder cancer and cholangiocarcinoma are aggressive biliary malignancies with poor prognoses, due to their insidious onset and late-stage diagnosis. Early detection is critical for improving survival outcomes yet remains challenging as early disease lacks specific symptoms and can mimic benign conditions. This article highlights the importance of risk stratification and multimodality imaging in the early identification of these cancers. Emerging strategies integrating artificial intelligence and deep learning models show promise in augmenting diagnostic accuracy. A multidisciplinary approach combining risk-based surveillance, standardized high-quality imaging, and novel diagnostic technologies is essential to bridge the gap in early detection and improve patient outcomes.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cause of cancer-related deaths and has a dismal prognosis due to late-stage diagnosis. Early detection, improves survival, but remains a challenge due to nonspecific symptoms and lack of population-level screening tools. Current guidelines recommend surveillance in individuals with >5% lifetime risk. Imaging modalities form the cornerstone of screening and surveillance, while serum biomarkers show promise. Emerging technologies in imaging are demonstrating potential to detect subtle changes years before clinical diagnosis. Together, multidisciplinary surveillance strategies may have the potential to shift the paradigm towards early detection in high-risk groups to improve outcomes.

Ultrasound is the standard first-line imaging modality for hepatocellular carcinoma (HCC) screening; however, in certain patient populations, sensitivity is limited, and thus alternative strategies are needed in these patients. This article will provide an overview of MR imaging and computed tomography (CT) for the screening of HCC, with an emphasis on abbreviated MR imaging protocols and recent advancements in CT technology (dual energy and photon-counting CT).

LI-RADS v2024 offers increased sensitivity in identifying hepatocellular carcinoma. All practices performing surveillance ultrasound for HCC should consider its adoption. This article reviews technical requirements and recommendations on implementation, and creating and maintaining a successful screening program, as well as reviews the current evidence that supports its adoption and use.

Lung cancer screening with low-dose computed tomography (CT) reduces mortality but requires integrated, system-based implementation. Review of evolving screening strategies highlighting risk-prediction models, imaging advances, and Lung CT Screening Reporting and Data System (Lung-RADS) as a standardized framework linking findings to management. Current low-dose chest CT requires comparing with earliest CT to detect change; Lung-RADS categories guide management with increasing complexity. AI enhances nodules detection and risk stratification but requires radiologist oversight; computer-aided detection supports consistency and longitudinal tracking. Implementation demands scalable infrastructure, targeted outreach, and continuous quality improvement to maximize survival benefits and equity.

This article provides a comprehensive overview of breast cancer screening guidelines and the evolving role of established and emerging imaging technologies. It reviews the proven mortality benefit of mammography, the widespread adoption of digital breast tomosynthesis, and the importance of MR imaging for high-risk populations. Supplemental modalities such as abbreviated MR imaging, contrast-enhanced mammography, whole-breast ultrasound, and molecular breast imaging are discussed, with emphasis on their advantages, limitations, and appropriate use. The article also explores the expanding role of artificial intelligence in improving screening accuracy, efficiency, and individualized risk assessment, highlighting future directions in personalized screening.

Whole-body (WB) MR imaging is practical for staging and monitoring cancers such as multiple myeloma, prostate and breast cancers. Increasingly, WB MR imaging is increasingly used to provide reassurance and detect cancers opportunistically. This article details the Oncologically relevant findings Reporting and Data System for consistent image acquisition, interpretation, and reporting. Studies show cancer detection rates of about 1% to 2% in asymptomatic populations. Challenges include high rates of incidental findings; however, multiparametric characterization limits overdiagnosis. Clear communication with participants and physicians, the careful use of interventions, and expert interpretation are vital to minimizing harm.