Nucleo
Automated CT scan analysis for oncology care, providing insights for tumor characterization and treatment.
Website: https://nucleoresearch.com
Cover Block
PUBLIC
| Name | Nucleo |
| Tagline | Automated CT scan analysis for oncology care, providing insights for tumor characterization and treatment. |
| Headquarters | San Francisco, California |
| Founded | 2025 |
| Stage | Seed |
| Business Model | SaaS |
| Industry | Healthtech |
| Technology | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | Undisclosed (total disclosed ~$4,000,000) |
Links
PUBLIC
- Website: https://nucleoresearch.com
- LinkedIn: https://www.linkedin.com/company/nucleoresearch
- Product Hunt: https://www.producthunt.com/products/nucleo-research-inc
Executive Summary
PUBLIC
Nucleo automates the analysis of CT scans for oncology, aiming to replace a manual and time-consuming clinical workflow with a software platform that delivers consistent, quantitative metrics in seconds [Nucleo homepage]. The company's immediate wedge is a SaaS tool that performs three specific tasks for oncologists and radiologists: body composition and sarcopenia assessment, tumor lesion sizing, and the classification of target versus non-target lesions according to RECIST criteria [Nucleo homepage, 2026]. Its technology is already in use at several leading U.S. hospitals, including Stanford Hospital and Cedars-Sinai, where validation studies have reported 98% agreement with medical experts and processing speeds claimed to be 2,500 times faster than manual methods [Y Combinator].
Founded in 2025 by Angelica Iacovelli and Luca Pegolotti, the team brings academic and industry experience from Stanford University and Apple's Health AI group, a background directly relevant to the product's development [Forbes, 2026] [Luca Pegolotti personal website, 2026]. Nucleo completed the Y Combinator Fall 2025 batch and has raised an estimated $4 million in seed capital, though the lead investor and detailed terms are not publicly disclosed. The business model is SaaS, with the company stating it is already generating revenue [Product Hunt].
Over the next 12 to 18 months, the key signals to monitor will be the expansion of its hospital partnerships beyond the initial validation sites, the publication of peer-reviewed clinical validation data, and the evolution of its platform from a focused workflow tool toward the broader "infrastructure layer for automated cancer diagnostics" it envisions [Product Hunt].
Data Accuracy: YELLOW -- Core product claims are confirmed by the company's website and Y Combinator materials. Team backgrounds are partially corroborated by LinkedIn and personal websites. Funding amount is estimated; lead investor is unconfirmed.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Seed |
| Business Model | SaaS |
| Industry / Vertical | Healthtech |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding | Undisclosed (~$4,000,000 estimated) |
Company Overview
PUBLIC
Nucleo Research, Inc. was founded in 2025 and is headquartered in San Francisco, California [LinkedIn]. The company emerged from the Y Combinator Fall 2025 cohort, a common origin point for its current public profile [Y Combinator]. Its founding story, as presented in launch materials, centers on applying artificial intelligence to a specific, time-intensive clinical workflow: the manual analysis of CT scans in oncology [Y Combinator].
The founders, Angelica Iacovelli and Luca Pegolotti, brought academic and industry experience from Stanford University and Apple's Health AI team to the venture [Product Hunt]. A key early milestone was the development of a platform capable of automating three distinct analytical tasks from a single CT scan upload: body composition assessment, tumor lesion sizing, and lesion classification according to RECIST criteria [Nucleo homepage]. By early 2026, the company reported its tools were being used in studies at several leading U.S. hospitals, including Stanford Hospital and Cedars-Sinai [Y Combinator].
Data Accuracy: YELLOW -- Core company details are confirmed via LinkedIn and YC materials, but specific founding dates and early funding details lack independent public corroboration.
Product and Technology
MIXED
The company's platform is designed to automate a specific, labor-intensive bottleneck in oncology workflows. Nucleo's AI takes a clinician's CT scan and produces a structured analysis focused on three core tasks, a workflow the company says delivers results in seconds [Nucleo homepage]. This positions the tool as a productivity aid rather than a diagnostic replacement, integrating into existing hospital systems to augment human expertise.
The product surfaces handle distinct but related clinical assessments. Body composition analysis automatically quantifies fat and muscle mass from a scan, a key indicator of sarcopenia which is critical for patient prognosis and treatment planning [Nucleo homepage, 2026]. Tumor lesion sizing provides precise and consistent volumetric measurements, replacing manual caliper-based methods [Nucleo homepage, 2026]. RECIST classification then automatically categorizes lesions as target or non-target according to standardized oncology criteria, a step essential for clinical trial enrollment and therapy response tracking [Nucleo homepage]. The company claims this integrated process is 2,500 times faster than manual segmentation and shows 98% agreement with medical expert review in validation studies [Y Combinator].
Technically, the stack is inferred from the product's function and available team backgrounds. The core likely involves computer vision models trained on proprietary datasets of annotated medical imagery, with a cloud-based inference layer to handle the processing workload. Founders' prior roles at Apple's Health AI team and Stanford's medical research groups suggest deep experience in developing and validating clinical AI algorithms [Product Hunt]. The public framing of the platform as "agentic" hints at a longer-term architectural vision where multiple specialized AI models orchestrate a more comprehensive diagnostic workflow [Y Combinator], though the current shipped features represent a focused, initial wedge.
Data Accuracy: YELLOW -- Product claims are confirmed by the company's own materials and Y Combinator launch post. Performance metrics (98% agreement, 2,500x speed) are sourced solely from the company's YC launch materials.
Market Research
PUBLIC The market for AI-powered oncology diagnostics is being pulled by a convergence of clinical need, technological feasibility, and economic pressure within healthcare systems.
Quantifying the total addressable market is challenging at this early stage, as Nucleo operates at the intersection of several large, adjacent markets. The global market for AI in medical imaging was valued at approximately $1.5 billion in 2023, with oncology applications representing a primary growth segment [GlobalData, 2024]. A more direct analog is the market for oncology clinical decision support software, which some analysts project to reach $2.8 billion by 2028 [Signify Research, 2024]. Nucleo’s initial product surfaces,automated body composition analysis and RECIST-based tumor tracking,address specific procedural needs within these broader categories. The company’s focus on CT scans, which account for over 80 million procedures annually in the U.S. alone [American College of Radiology, 2023], provides a clear entry point into a high-volume diagnostic workflow.
Demand is driven by persistent, well-documented pain points in clinical oncology. Manual segmentation and measurement of tumors on CT scans is a time-consuming, subjective process that contributes to radiologist burnout and inter-reader variability. The cited research claims Nucleo’s tools can perform segmentation 2,500 times faster than manual methods, directly addressing this workflow bottleneck [Y Combinator]. Furthermore, the growing emphasis on quantitative biomarkers in oncology, such as muscle mass (sarcopenia) for predicting patient outcomes and chemotherapy tolerance, creates a need for standardized, automated measurement that manual workflows struggle to deliver consistently [Journal of Cachexia, Sarcopenia and Muscle, 2023].
Key adjacent and substitute markets illustrate both the scope of the opportunity and the competitive landscape. The broader field of digital pathology, which applies similar AI techniques to tissue samples, is a parallel growth area. More directly, the market for clinical trial imaging services, where contract research organizations (CROs) manually assess tumor response, represents a multi-billion dollar service industry that could be disrupted by automation [ICON plc Annual Report, 2023]. Nucleo’s cited work with academic medical centers like Stanford Hospital and Cedars-Sinai suggests an initial wedge into research and clinical trial support, which could later expand into broader diagnostic use [Y Combinator LinkedIn post].
Regulatory and macro forces present a complex but navigable environment. The U.S. Food and Drug Administration (FDA) has established a clear, if rigorous, pathway for software as a medical device (SaMD), particularly for radiology AI. Over 500 AI/ML-enabled medical devices have now received FDA clearance, the majority in radiology [FDA, 2024]. This regulatory precedent lowers the barrier for market entry compared to a novel therapeutic. Macro forces are equally supportive: healthcare systems face intensifying pressure to improve efficiency and standardize care, while an aging global population is expected to increase cancer incidence rates, straining diagnostic capacity [World Health Organization, 2023].
| Metric | Value |
|---|---|
| AI in Medical Imaging (2023) | 1.5 $B |
| Oncology CDS Software (2028 Proj.) | 2.8 $B |
| Annual U.S. CT Procedures | 80 million |
| FDA-Cleared AI/ML Devices | 500 units |
The available sizing data, while broad, confirms the company is operating in large, established markets with clear regulatory pathways. The scale of annual CT procedures underscores the volume potential for a workflow tool, even capturing a single-digit percentage of scans.
Data Accuracy: YELLOW -- Market sizing figures are from third-party analyst reports and public health statistics, providing a reasonable analog. Specific demand drivers and regulatory counts are publicly documented. Nucleo's precise SAM/SOM is not publicly modeled.
Competitive Landscape
MIXED Nucleo enters a specialized segment of medical imaging AI where the competitive pressure is defined not by a single dominant player but by a collection of focused challengers and large, diversified incumbents.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Nucleo | Automated CT scan analysis for oncology care, focusing on body composition, lesion sizing, and RECIST classification. | Seed (YC F25); total disclosed ~$4M [PUBLIC] | Integrates sarcopenia assessment with tumor response tracking in a single workflow; cites 2,500x speedup over manual methods [Y Combinator]. | [Nucleo homepage] |
| Nucleai | AI-powered spatial biology platform for pathology image analysis, primarily in immuno-oncology. | Venture; $46M Series B (2022) [PUBLIC] | Focus on biomarker discovery and predictive modeling from histology slides, not CT scans. | [Crunchbase, 2022] |
| Brainomix | AI-powered imaging biomarkers for stroke, lung fibrosis, and oncology (e.g., lung nodule assessment). | Venture; £16M Series B (2022) [PUBLIC] | Broader multi-modality (CT, MRI) and multi-therapeutic area platform with regulatory clearances (CE Mark, UKCA). | [Brainomix, 2022] |
The table highlights a fragmented competitive map. Nucleai operates in the adjacent but distinct domain of digital pathology, analyzing tissue slides rather than CT scans. Brainomix, while also analyzing CTs, has built its commercial footprint in stroke and lung disease, with oncology as a more recent expansion. This leaves the specific niche of automated, quantitative CT analysis for comprehensive oncology workflow,encompassing both body composition (sarcopenia) and tumor response,relatively open for a focused entrant.
The segment-by-segment map shows three primary competitive vectors. First, large incumbent medical imaging vendors like GE HealthCare and Siemens Healthineers embed AI tools into their scanner and PACS software, but these are often modality-agnostic and not purpose-built for the nuanced RECIST tracking and sarcopenia assessment Nucleo emphasizes. Second, pure-play AI startups are carving out specific clinical pathways: PathAI in pathology, Viz.ai in neurovascular, and Aidoc in radiology triage. Third, adjacent substitutes include contract research organizations (CROs) and manual reading services that provide tumor measurement for clinical trials, which are the legacy workflow Nucleo aims to displace with automation [Y Combinator].
Nucleo's defensible edge today appears to be its integrated workflow and early academic validation. The platform's ability to handle both body composition (a prognostic factor for cancer outcomes) and RECIST-standard tumor measurements in one analysis is a specific combination not broadly marketed by the named competitors. Furthermore, its cited 98% agreement with medical experts and deployment in institutions like Stanford Hospital and Cedars-Sinai provide initial clinical credibility [Y Combinator]. This edge is perishable, however, as it relies on maintaining a technological lead in algorithm accuracy and speed. The talent background of the founders in Stanford and Apple Health AI is a current asset for R&D, but it is not a permanent moat.
The company is most exposed on two fronts. First, from a commercial and regulatory standpoint, Brainomix and similar players have already navigated the CE Mark and UKCA approval processes for their AI tools, a path Nucleo must also travel for broader European adoption. Second, from a product expansion perspective, Nucleo's focus on CT limits its addressable market compared to platforms that analyze multiple imaging modalities (CT, MRI, PET). A competitor with a broader modality-agnostic platform could decide to build or acquire the specific oncology CT capabilities Nucleo offers, leveraging an existing sales channel into radiology departments.
The most plausible 18-month scenario involves increased segmentation within oncology AI. A winner in this niche will be the company that successfully transitions from pilot studies at academic hospitals to scaled commercial contracts with community oncology networks and pharmaceutical trial sponsors. If Nucleo can convert its early hospital partnerships into multi-site enterprise SaaS contracts and secure its first regulatory clearance, it becomes a compelling acquisition target for a larger life sciences tools company. A loser in this scenario would be a company that remains confined to academic validation studies without demonstrating clear revenue growth or workflow integration beyond a handful of flagship institutions. The competitive risk is less about being out-engineered and more about being out-commercialized by a rival with a stronger go-to-market motion for the hospital and CRO buyer.
Data Accuracy: YELLOW -- Competitor funding and positioning are confirmed by public filings and company materials. Nucleo's differentiation and validation metrics are sourced from its Y Combinator launch materials.
Opportunity
PUBLIC The prize for Nucleo is a foundational role in the digitization of oncology care, where its automated analysis could become the standard for interpreting the millions of CT scans performed annually in cancer diagnosis and treatment monitoring.
The headline opportunity is to become the default infrastructure layer for oncology diagnostics, a position that would see its software embedded in the clinical workflows of major hospital systems and pharmaceutical trials. This outcome is reachable because the company has already secured initial validation with prestigious institutions like Stanford Hospital and Cedars-Sinai, demonstrating its technology's fit within existing, high-stakes environments [Y Combinator]. The core product directly addresses a critical bottleneck, manual image analysis, with a claimed 2,500x speed improvement, a metric that speaks directly to the economic and operational pain point it aims to solve [Y Combinator]. By starting with a focused wedge on CT analysis for oncology, the path to becoming the underlying platform for a broader range of cancer diagnostics is a logical, if challenging, extension of its current work.
Several concrete paths could drive this platform vision to massive scale. The scenarios below outline how Nucleo might expand its footprint from early clinical studies to a dominant market position.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Hospital System Standardization | Nucleo's tools become the mandated software for tumor measurement and RECIST classification across a major integrated delivery network's oncology department. | A multi-year enterprise licensing agreement with one of its current pilot partners, such as Cedars-Sinai or UCI Health. | The company is already "powering studies" in leading U.S. hospitals, indicating integration into research workflows that often precede broader clinical adoption [Product Hunt]. |
| Clinical Trial Infrastructure | The platform is adopted as the primary imaging analysis endpoint provider for a wave of new oncology drug trials, creating a recurring, high-value revenue stream. | A partnership announcement with a top-10 pharmaceutical company or a major Contract Research Organization (CRO). | Nucleo's automated RECIST classification is specifically built for clinical trial criteria, and its reported 98% agreement with medical experts provides the validation required for regulatory acceptance [Y Combinator]. |
What compounding looks like for Nucleo is a classic data and workflow flywheel. Each new hospital or trial partnership feeds the system with more diverse, real-world CT scan data. This data can be used to refine the AI models, improving accuracy and expanding the range of detectable conditions and anatomical variations. Improved models, in turn, make the platform more valuable and reliable for the next institution, lowering the barrier to adoption. Furthermore, once integrated into a hospital's radiology information system (RIS) or picture archiving and communication system (PACS), the switching costs and workflow entrenchment create a significant lock-in effect. The company's claim that it is "building the infrastructure layer" suggests this flywheel dynamic is a core part of its long-term strategy [Product Hunt].
The size of the win can be framed by looking at comparable companies that have carved out specialized niches in medical imaging AI. For instance, Nucleai, a competitor focused on AI-powered pathology for oncology, has raised over $60 million and partners with major biopharma companies. While not a direct valuation comparable, it illustrates the scale of investment and ambition in adjacent oncology AI segments. If Nucleo successfully executes on the "Hospital System Standardization" scenario and captures a material share of the U.S. oncology imaging analysis market, a multi-billion dollar enterprise value is a plausible outcome for a category-defining platform. This is a scenario-based illustration, not a forecast, but it defines the magnitude of the opportunity the company is pursuing.
Data Accuracy: YELLOW -- Opportunity framing relies on company claims of hospital partnerships and performance metrics from Y Combinator materials; market size and comparable valuation context are not independently sourced from third-party reports.
Sources
PUBLIC
[Nucleo homepage] Nucleo Homepage | https://nucleoresearch.com
[Nucleo homepage, 2026] Nucleo Homepage | https://nucleoresearch.com
[Y Combinator] Y Combinator Launch Post for Nucleo | https://www.ycombinator.com/launches/Okn-nucleo-automated-cancer-diagnostics
[Forbes, 2026] Angelica Iacovelli Profile | https://www.forbes.com/profile/angelica-iacovelli/
[Luca Pegolotti personal website, 2026] Luca Pegolotti Personal Website | https://www.linkedin.com/in/luca-pegolotti/
[Product Hunt] Nucleo Product Hunt Page | https://www.producthunt.com/products/nucleo-research-inc
[LinkedIn] Nucleo LinkedIn Company Page | https://www.linkedin.com/company/nucleoresearch
[Y Combinator LinkedIn post] Y Combinator LinkedIn Launch Post | https://www.linkedin.com/posts/y-combinator_nucleo-yc-f25-helps-oncologists-and-radiologists-activity-7391882038653595648--oCh
[GlobalData, 2024] GlobalData Report on AI in Medical Imaging | https://www.globaldata.com/store/report/ai-in-medical-imaging-market-analysis/
[Signify Research, 2024] Signify Research Projection for Oncology CDS Software | https://www.signifyresearch.net/
[American College of Radiology, 2023] American College of Radiology CT Procedure Data | https://www.acr.org/Practice-Management-Quality-Informatics/Imaging-3/Case-Studies/CT
[Journal of Cachexia, Sarcopenia and Muscle, 2023] Journal of Cachexia, Sarcopenia and Muscle | https://onlinelibrary.wiley.com/journal/21903160
[ICON plc Annual Report, 2023] ICON plc Annual Report | https://investor.iconplc.com/financial-information/annual-reports
[FDA, 2024] FDA Data on AI/ML-Enabled Medical Devices | https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
[World Health Organization, 2023] World Health Organization Cancer Incidence Report | https://www.who.int/news-room/fact-sheets/detail/cancer
[Crunchbase, 2022] Crunchbase Profile for Nucleai | https://www.crunchbase.com/organization/nucleai
[Brainomix, 2022] Brainomix Series B Announcement | https://www.brainomix.com/news/brainomix-raises-ps16m-series-b-funding-to-expand-ai-imaging-biomarker-platform/
Articles about Nucleo
- Nucleo's AI Reads the CT Scan for 98% Agreement With the Oncologist — The YC F25 startup is already working with Stanford Hospital and Cedars-Sinai to automate tumor sizing and body composition analysis.