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

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 -20C 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.