Problem

Nucleo targets the operational bottleneck between cancer diagnosis and treatment. Its YC materials describe cancer as partly a “logistical failure”: U.S. patients typically wait about 9–11 weeks from diagnosis to treatment, while delays after diagnosis are associated with materially higher mortality. The underlying work is slow and costly because radiologists and oncologists must manually extract body-composition, sarcopenia, tumor-measurement, and treatment-response information from CT scans, with avoidable variability and error.

The killer use case is accelerating oncology decision-making from a CT scan: quickly determining tumor burden and response while also assessing body composition and sarcopenia, information that can affect treatment planning and dosing. Nucleo’s stated ambition is to reduce the diagnosis-to-treatment interval from roughly 9–11 weeks to no more than 10 days by automating these steps.

Product / Service

Nucleo is an AI-first medtech software platform for oncology CT scans. A clinician imports a CT scan, after which the system automatically extracts structured outputs including body-composition and sarcopenia measures, lesion volume and sizing, and target-versus-non-target lesion classification under RECIST criteria. The company positions the product as an agentic platform that reads scans end to end and produces clinically useful results and reports in seconds.

The benefit is faster, more consistent analysis without requiring manual segmentation for every case. Nucleo reports segmentation that is 2,500 times faster than manual work, state-of-the-art sarcopenia performance, and 98% agreement with medical experts in its validation studies. The initial workflow centers on three related oncology tasks: body-composition assessment, tumor-lesion sizing, and automated lesion classification.

Market

Nucleo competes in AI-enabled medical imaging and oncology clinical-workflow software, selling into hospitals and potentially clinical-trial imaging teams, oncologists, and radiologists. Its closest adjacent competitors include Mint Medical’s mint Lesion, which combines tumor-response assessment with structured radiology and clinical-trial workflows, and Median Technologies’ iSee and imaging-CRO platform, which identifies, quantifies, and tracks lesions across clinical-trial time points. Nucleo’s differentiation is its emphasis on automated CT analysis and agentic extraction of multiple oncology metrics rather than primarily serving as a structured workstation or trial-services platform.

The company is an early-stage YC Fall 2025 startup founded in 2025 and listed as active, with a very small team. It reports work with Stanford Hospital, Cedars-Sinai, UCI Health, and Weill Cornell, which is meaningful early validation and access to prospective clinical users. Public materials reviewed do not disclose revenue or confirm FDA clearance, so Nucleo should be characterized as an early commercial/clinical-validation company rather than a scaled revenue business; its pre-revenue status cannot be established definitively from the available evidence.