Canine olfaction combined with Bayesian modeling for multi-cancer detection from breath samples: a Phase-2 study in India

Abstract PURPOSE Low-cost, acceptable, and high-sensitivity triage tests are needed to address the challenge of low cancer prevalence in population screening, particularly in low- and middle-income countries (LMICs). Breath-based canine olfaction has the potential to serve this role; however, evidence to date has been mostly limited to high-income countries and relatively small, single-cancer studies. We evaluated the analytical validity of a multi-cancer breath detection system using trained dogs and Bayesian fusion modeling. PATIENTS AND METHODS We conducted an assessor-masked, multi-center case–control study across six hospitals in Karnataka, India (March 2024–June 2025; CTRI/2024/10/075938). A total of 3,275 participants were enrolled: 1,773 for training and 1,502 for testing. The test cohort comprised 283 treatment-naive, biopsy-confirmed cancer patients (seven major cancer groups) and 1,219 controls (healthy, non-oncologic chronic disease, and benign biopsy). Breath was collected on cotton masks, stored under −20°C cold-chain conditions, and presented on a sniffing platform to trained detection dogs. Individual responses were integrated with Bayesian fusion incorporating historical dog performance and participant-level sample variables. RESULTS The fusion system achieved 91.5% sensitivity (95% CI, 88.0 – 94.8) and 90.8% specificity (95% CI, 89.1 – 92.5), with an area under the ROC curve (AUC) of 0.962 (95% CI, 0.951 - 0.971). Sensitivity was 89.6% in early-stages (Stage I–II), and was relatively consistent across major cancer types. CONCLUSION In a 1,502-participant test cohort, canine olfaction–Bayesian fusion achieved high accuracy for multi-cancer detection from breath, with stable performance across stages. These data establish the analytical validity and support the prospective evaluation of true screening populations. Context Summary Key Objective Does canine olfaction combined with Bayesian modeling maintain analytical validity for multi-cancer breath screening in a large assessor-masked study in India? Knowledge Generated Detection dogs achieved sensitivity and specificity above 90% (AUC 0.962) in 1502 participants, with comparable performance across early- and late-stage cancers.

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