Projecting the Impact of Multi-Cancer Early Detection on Late-Stage Incidence Using Multi-State Disease Modeling.

BACKGROUND: Downstaging-reduction in late-stage incidence-has been proposed as an endpoint in randomized trials of multi-cancer early detection (MCED) tests. How downstaging depends on test performance and follow-up has been studied for some cancers but is understudied for cancers without existing screening and for MCED tests that include these cancer types. METHODS: We develop a model for cancer natural history that can be fit to registry incidence patterns under minimal inputs and can be estimated for solid cancers without existing screening. Fitted models are combined to project downstaging in MCED trials given sensitivity for early- and late-stage cancers. We fit models for 12 cancers using incidence data from the Surveillance, Epidemiology, and End Results program and project downstaging in a simulated trial under variable preclinical latencies and test sensitivities. RESULTS: A proof-of-principle lung cancer model approximated downstaging in the National Lung Screening Trial. Given published stage-specific sensitivities for 12 cancers, we projected downstaging ranging from 21% to 43% across plausible preclinical latencies in a hypothetical 3-screen MCED trial. Late-stage incidence reductions manifest soon after screening begins. Downstaging increases with longer early-stage latency or higher early-stage test sensitivity. CONCLUSIONS: Even short-term MCED trials could produce substantial downstaging given adequate early-stage test sensitivity. IMPACT: Modeling the natural histories of cancers without existing screening facilitates analysis of novel MCED products and trial designs. The framework informs expectations of MCED impact on disease stage at diagnosis and could serve as a building block for designing trials with late-stage incidence as the primary endpoint.

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Short-term Endpoints for Cancer Screening Trials: Does Tumor Subtype Matter?

Multicancer early detection tests are precipitating a reexamination of potential short-term endpoints for cancer screening trials. A reduction in advanced stage incidence is a prime candidate, and stage-shift models that substitute early-stage for late-stage survival have been used to predict mortality reduction due to screening. However, standard stage-shift models often ignore prognostic subtypes, effectively implying that cancers detected early also have an associated subtype shift. To illustrate the differences between mortality predictions from stage-shift models that ignore versus preserve prognostic subtype, we use ovarian cancer partitioned by histologic subtype and prostate cancer partitioned by grade. We infer general conditions under which stage-shift models that preserve prognostic subtype are likely to predict mortality reductions that differ from those that ignore subtype and examine the implications for short-term endpoints based on stage in cancer screening trials.

Modeling the impact of novel systemic treatments on lung cancer screening benefits.

BACKGROUND: Since low-dose computed tomography (LDCT) screening was shown to be effective in the National Lung Screening Trial (NLST), novel targeted therapies and immunotherapies for advanced lung cancer have become available. This study investigated the impact of these treatment advances on the expected benefits of LDCT screening. METHODS: A microsimulation model of LDCT screening for high-risk individuals under standard systemic treatments (chemotherapy and radiation therapy) and novel treatments (immunotherapy and targeted therapy) was used. The model assumed a reduction in advanced-stage disease consistent with the NLST, and given the stage at diagnosis, it projected survival. The disease-specific relative mortality reduction (MR) due to LDCT screening was projected in the trial setting and in a population eligible for LDCT screening under the current US Preventive Services Task Force (USPSTF) recommendations. RESULTS: The availability of novel treatments reduced the MR in the LDCT arm of the NLST from 15% to 13.5% and the number of lung cancer deaths prevented from 310 to 224 per 100,000 persons screened. Over 10 years, population LDCT screening based on USPSTF recommendations prevented 374 lung cancer deaths per 100,000 under standard treatments (13.3% MR) and 236 per 100,000 under fully adopted novel treatments (10.6% MR). The number needed to screen to avert one death over 10 years was 270 under standard treatments and 440 under novel treatments. CONCLUSIONS: The transition from standard systemic treatments to novel treatments is expected to reduce the relative and absolute mortality benefits of LDCT screening. Benefit-harm tradeoffs of LDCT screening are likely to change as novel treatments become widespread.

Impact of novel systemic therapies on the first-year costs of care for melanoma among Medicare beneficiaries.

BACKGROUND: Since 2011, the therapeutic landscape of melanoma has changed dramatically because of the adoption of immune checkpoint inhibitor and targeted therapies. The authors sought to quantify the effects of these changes on short-term treatment costs by comparing the first-year cancer-attributable costs in novel (2011-2015) and historical (2004-2010) treatment eras. METHODS: The authors estimated the first-year cancer-attributable and out-of-pocket (OOP) costs by cancer stage at diagnosis by using a case-control approach. Patients aged ≥67 years with melanoma results were used to calculate the total direct costs of treatment during the first year after the diagnosis of melanoma in the US Medicare population older than 65 years. Costs were reported in 2018 dollars. RESULTS: Costs increased with the stage at diagnosis. Average first-year cancer-attributable costs per patient for stage IV patients increased significantly by 61.7% from $45,952 to $74,297 after the adoption of novel treatments. Per-patient OOP responsibility decreased by almost 30.8% across all stages of cancer but increased by 16.5% for stage IV patients from 2004 ($7646) to 2015 ($8911). The total direct cost of treatment for persons with melanoma older than 65 years increased by $16.03 million (4.93%) from $324.68 million in 2010 to $340.71 million in 2015. The largest increase in yearly total cost, $23.64 million (56.53%), was observed among stage IV patients. CONCLUSIONS: The direct cost of melanoma increased significantly in the Medicare population, particularly for advanced-stage disease. Prevention and early detection initiatives may reduce the economic burden of melanoma.

Strategizing Screening for Melanoma in an Era of Novel Treatments: A Model-Based Approach.

BACKGROUND: Benefit-harm tradeoffs of melanoma screening depend on disease risk and treatment efficacy. We developed a model to project outcomes of screening for melanoma in populations with different risks under historic and novel systemic treatments. METHODS: Computer simulation model of a screening program with specified impact on overall and advanced-stage incidence. Inputs included meta-analyses of treatment trials, cancer registry data, and a melanoma risk prediction study RESULTS: Assuming 50% reduction in advanced stage under screening, the model projected 59 and 38 lives saved per 100,000 men under historic and novel treatments, respectively. With 10% increase in stage I, the model projects 2.9 and 4.7 overdiagnosed cases per life saved and number needed to be screened (NNS) equal to 1695 and 2632 under historical and novel treatments. When screening was performed only for the 20% of individuals with highest predicted risk, 34 and 22 lives per 100,000 were saved under historic and novel treatments. Similar results were obtained for women, but lives saved were lower. CONCLUSIONS: Melanoma early detection programs must shift a substantial fraction of cases from advanced to localized stage to be sustainable. Advances in systemic therapies for melanoma might noticeably reduce benefits of screening, but restricting screening to individuals at highest risk will likely reduce intervention efforts and harms while preserving >50% of the benefit of nontargeted screening. IMPACT: Our accessible modeling framework will help to guide population melanoma screening programs in an era of novel treatments for advanced disease.