Abstract

Rising cancer care costs impose financial burdens on health systems. Applying artificial intelligence to diagnostic algorithms may reduce testing costs and avoid wasteful therapy-related expenditures. To evaluate the financial and clinical impact of incorporating artificial intelligence-based determination of mismatch repair/microsatellite instability status into the first-line metastatic colorectal carcinoma setting, we developed a deterministic model to compare eight testing strategies: A) next-generation sequencing alone, B) high-sensitivity polymerase chain reaction or immunohistochemistry panel alone, C) high-specificity panel alone, D) high-specificity artificial intelligence alone, E) high-sensitivity artificial intelligence followed by next generation sequencing, F) high-specificity artificial intelligence followed by next-generation sequencing, G) high-sensitivity artificial intelligence and high-sensitivity panel, and H) high-sensitivity artificial intelligence and high-specificity panel. We used a hypothetical, nationally representative, population-based sample of individuals receiving first-line treatment for de novo metastatic colorectal cancer (N = 32,549) in the United States. Model inputs were derived from secondary research (peer-reviewed literature and Medicare data). We estimated the population-level diagnostic costs and clinical implications for each testing strategy. The testing strategy that resulted in the greatest project cost savings (including testing and first-line drug cost) compared to next-generation sequencing alone in newly-diagnosed metastatic colorectal cancer was using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel for patients testing negative by artificial intelligence ($400 million, 12.9%). The high-specificity artificial intelligence-only strategy resulted in the most favorable clinical impact, with 97% diagnostic accuracy in guiding genotype-directed treatment and average time to treatment initiation of less than one day. Artificial intelligence has the potential to reduce both time to treatment initiation and costs in the metastatic colorectal cancer setting without meaningfully sacrificing diagnostic accuracy. We expect the artificial intelligence value proposition to improve in coming years, with increasing diagnostic accuracy and decreasing costs of processing power. To extract maximal value from the technology, health systems should evaluate integrating diagnostic histopathologic artificial intelligence into institutional protocols, perhaps in place of other genotyping methodologies.

Highlights

  • Oncologic diagnostic algorithms, those involving nextgeneration sequencing (NGS), financially burden healthcare systems

  • We generated eight potential diagnostic algorithms for determining MMR/MSI status in the U.S first-line newly-diagnosed metastatic CRC (mCRC) population: NGS alone, high-sensitivity polymerase chain reaction (PCR) or IHC panel (“panel” for short) alone, high-specificity panel alone, highspecificity artificial intelligence (AI) alone, high-sensitivity AI with confirmatory NGS for patients testing negative by AI, high-specificity AI with confirmatory NGS for patients testing positive by AI, highsensitivity AI with confirmatory high-sensitivity panel for patients testing positive by AI, and high-sensitivity AI with confirmatory high-specificity panel for patients testing positive by AI (Figure 1)

  • The high-specificity AI-only scenario was associated with the shortest time to treatment initiation (

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Summary

Introduction

Those involving nextgeneration sequencing (NGS), financially burden healthcare systems. Just as the advent of NGS was an advancement over polymerase chain reaction (PCR) and immunohistochemistry (IHC) for some applications, artificial intelligence (AI) may be the innovative oncologic diagnostic agent. AI can recapitulate genetic information with area under the receiver-operator curve (ROC) approaching 0.9 [1, 2]. AI may help overcome NGS-related challenges like cost, packing and shipping delays, and turnaround time. AI costs, following initial investment, would be a fraction of other technologies’ costs. Since tumors grow in the absence of treatment, AI’s faster turnaround (and associated earlier treatment initiation) could impact clinical outcomes

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