AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis.

PURPOSE: To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms. METHODS: Markov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care ("treat none"; no intervention regardless of opportunistic CT findings), (2) universal statin therapy ("treat all" for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts. RESULTS: For the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the "treat none" and "treat all" strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario. CONCLUSION: AI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.

Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study.

BACKGROUND: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. METHODS: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. FINDINGS: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. INTERPRETATION: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality. FUNDING: European Commission and Japan Society of Promotion of Science.

Cost-effectiveness of colorectal cancer screening programmes using sigmoidoscopy and immunochemical faecal occult blood test.

OBJECTIVE: Several European countries are implementing organized colorectal cancer (CRC) screening programmes using faecal immunochemical test (FIT) and/or flexible sigmoidoscopy (FS), but the cost-effectiveness of these programmes is not yet available. We aimed to assess cost-effectiveness, based on data from the established Piedmont screening programme. METHODS: Using the Piedmont programme data, a Markov model was constructed comparing three strategies in a simulated cohort of 100,000 subjects: single FS, biennial FIT, or sequential strategy (FS + FIT offered to FS non-responders). Estimates for CRC incidence and mortality prevention were derived from studies of organized screening. Cost analysis for FS and FIT was based on data from organized programmes. Incremental cost-effectiveness ratios (ICER) between the different strategies were calculated. Sensitivity and probabilistic analyses were performed. RESULTS: Direct costs for FS, and for FIT at first and subsequent rounds, were estimated as €160, €33, and €21, respectively. All the simulated strategies were effective (10-17% CRC incidence reduction) and cost-effective vs. no screening (ICER <€1000 per life-year saved). FS and FS + FIT were the only cost-saving strategies, with FS least expensive (€15 saving per person invited). FS + FIT and FS were the only non-dominated strategies. FS + FIT were more effective and cost-effective than FS (ICER €1217 per life-year saved). The residual marginal uncertainty was mainly related to parameters inherent to FIT effectiveness and adherence. CONCLUSIONS: Organized CRC screening programmes are highly cost-effective, irrespective of the test selected. A sequential approach with FS and FIT appears the most cost-effective option. A single FS is the least expensive, but convenient, approach.

Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset.

OBJECTIVES: To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. METHODS: Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from nine centers and six different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. A panel majority report (PMR) was used as reference standard. Agreement (κ) and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification. RESULTS: While the agreement of individual radiologists with the PMR ranged from κ = 0.483 to κ = 0.885, the ABDE correctly classified 563/613 mammograms (92 %). A substantial agreement for binary classification was found for individual reader pairs (κ = 0.620, standard deviation [SD] = 0.140), individual versus PMR (κ = 0.736, SD = 0.117), and individual versus ABDE (κ = 0.674, SD = 0.095). Agreement between ABDE and PMR was almost perfect (κ = 0.831). CONCLUSIONS: The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation. KEY POINTS: Individual BD assessment differs from PMR with κ as low as 0.483. An ABDE correctly classified 92 % of mammograms with almost perfect agreement (κ = 0.831). An ABDE can be a valid alternative to subjective BD assessment.

Population screening for colorectal cancer by flexible sigmoidoscopy or CT colonography: study protocol for a multicenter randomized trial.

BACKGROUND: Colorectal cancer (CRC) is the second most prevalent type of cancer in Europe. A single flexible sigmoidoscopy (FS) screening at around the age of 60 years prevents about one-third of CRC cases. However, FS screens only the distal colon, and thus mortality from proximal CRC is unaffected. Computed tomography colonography (CTC) is a highly accurate examination that allows assessment of the entire colon. However, the benefit of CTC testing as a CRC screening test is uncertain. We designed a randomized trial to compare participation rate, detection rates, and costs between CTC (with computer-aided detection) and FS as primary tests for population-based screening. METHODS/DESIGN: An invitation letter to participate in a randomized screening trial comparing CTC versus FS will be mailed to a sample of 20,000 people aged 58 or 60 years, living in the Piedmont region and the Verona district of Italy. Individuals with a history of CRC, adenomas, inflammatory bowel disease, or recent colonoscopy, or with two first-degree relatives with CRC will be excluded from the study by their general practitioners. Individuals responding positively to the invitation letter will be then randomized to the intervention group (CTC) or control group (FS), and scheduled for the screening procedure. The primary outcome parameter of this part of the trial is the difference in advanced neoplasia detection between the two screening tests. Secondary outcomes are cost-effectiveness analysis, referral rates for colonoscopy induced by CTC versus FS, and the expected and perceived burden of the procedures. To compare participation rates for CTC versus FS, 2,000 additional eligible subjects will be randomly assigned to receive an invitation for screening with CTC or FS. In the CTC arm, non-responders will be offered fecal occult blood test (FOBT) as alternative screening test, while in the FS arm, non-responders will receive an invitation letter to undergo screening with either FOBT or CTC. Data on reasons for participation and non-participation will also be collected. DISCUSSION: This study will provide reliable information concerning benefits and risks of the adoption of CTC as a mass screening intervention in comparison with FS. The trial will also evaluate the role of computer-aided detection in a screening setting. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01739608.

CT colonography: preliminary assessment of a double-read paradigm that uses computer-aided detection as the first reader.

PURPOSE: To compare diagnostic performance and time efficiency of double-reading first-reader computer-aided detection (CAD) (DR FR CAD) followed by radiologist interpretation with that of an unassisted read using segmentally unblinded colonoscopy as reference standard. MATERIALS AND METHODS: The local ethical committee approved this study. Written consent to use examinations was obtained from patients. Three experienced radiologists searched for polyps 6 mm or larger in 155 computed tomographic (CT) colonographic studies (57 containing 10 masses and 79 polyps ≥ 6 mm). Reading was randomized to either unassisted read or DR FR CAD. Data sets were reread 6 weeks later by using the opposite paradigm. DR FR CAD consists of evaluation of CAD prompts, followed by fast two-dimensional review for mass detection. CAD sensitivity was calculated. Readers' diagnoses and reviewing times with and without CAD were compared by using McNemar and Student t tests, respectively. Association between missed polyps and lesion characteristics was explored with multiple regression analysis. RESULTS: With mean rate of 19 (standard deviation, 14; median, 15; range, 4-127) false-positive results per patient, CAD sensitivity was 90% for lesions 6 mm or larger. Readers' sensitivity and specificity for lesions 6 mm or larger were 74% (95% confidence interval [CI]: 65%, 84%) and 93% (95% CI: 89%, 97%), respectively, for the unassisted read and 77% (95% CI: 67%, 85%) and 90% (95% CI: 85%, 95%), respectively, for DR FR CAD (P = .343 and P = .189, respectively). Overall unassisted and DR FR CAD reviewing times were similar (243 vs 239 seconds; P = .623); DR FR CAD was faster when the number of CAD marks per patient was 20 or fewer (187 vs 220 seconds, P <01). Odds ratio of missing a polyp with CAD decreased as polyp size increased (0.6) and for polyps visible on both prone and supine scans (0.12); it increased for flat lesions (9.1). CONCLUSION: DR FR CAD paradigm had similar performance compared with unassisted interpretation but better time efficiency when 20 or fewer CAD prompts per patient were generated.