Altis Labs

We’re proud to present another study at #ESMO25 this weekend, illustrating how AI-measured changes in body composition predict survival differences in lung cancer patients. Using a multimodal real-world dataset of over 800 aNSCLC patients, our models measured changes in skeletal muscle volume (SMV) and subcutaneous fat volume (SFV) from CT scans taken at baseline and 8 weeks post first-line systemic treatment initiation. Key takeaway: Greater early losses in SMV or SFV were strongly associated with poorer survival, and these associations were sex-specific; muscle loss had a stronger detrimental association among males, while fat loss had a stronger detrimental association among females. What this means: Longitudinal quantification of body composition from standard CT imaging and their association with survival highlight the opportunity for imaging-based assessments beyond traditional measures of disease progression. Presentation Details: Poster: #1868P Date: Saturday, October 18, 12-1pm CET Category: NSCLC, metastatic

We’re excited to present our latest research at #ESMO25 this weekend! In a Phase 3 aNSCLC trial, we evaluated how IPRO Response Rate, our AI-based imaging endpoint, could serve as a potential early endpoint for overall survival (OS). Key takeaway: IPRO Response Rate (IRR), defined as % of subjects with a ≥30% improvement over baseline IPRO, had a stronger association with OS treatment effects compared to RECIST-based ORR (0.49 vs. 0.05) across SWOG-0819 subpopulations What this means: Using IRR as an exploratory endpoint could improve certainty of true efficacy, thereby informing go/no-go decisions, interim analyses, and future trial design. If you’re attending ESMO, come visit our poster to learn how AI-based endpoints are accelerating clinical development. Presentation Details: Poster: #1911P Date: Saturday, October 18, 12-1pm CET Category: NSCLC, metastatic

We’re excited to share that our latest research will be presented at ESMO 2025 in Berlin! This work highlights how IPRO can be applied as an early endpoint in oncology clinical trials, helping sponsors assess treatment effect earlier and more reliably. If you’ll be at #ESMO2025, let’s connect.

We’ll be at #BioTechX in Philadelphia, joining thousands of leaders exploring how AI can accelerate clinical development. We’ll be presenting a poster demonstrating how Altis Labs uses real-world imaging data to train outcome-prediction models. Our goal: make clinical development more efficient by enabling earlier efficacy signals in oncology trials. If you’re heading to BiotechX, let’s connect. Reach out to Felix Baldauf-Lenschen, Duoaud Shah, or Helen Z. to set up a meeting. BioTechX

Excited to attend this FDA x AACR workshop next week. It's an important discussion on how we can better measure success in cancer research. Especially looking forward to Dr. Lillian Siu's session on Evidence Gaps in Endpoint Development.

The FDA’s new draft guidance confirms overall survival (OS) as the “gold standard end point.” This raises the stakes: PFS and ORR, which often fail to reliably predict long-term survival, are no longer enough. As Endpoints News put it, sponsors now face greater regulatory and valuation risk unless they can provide OS data. Yet the FDA also notes: “If there is significant uncertainty in the OS results, but efficacy is supported by an intermediate clinical endpoint, accelerated approval may be the most appropriate pathway.” That underscores a critical challenge: today we lack robust intermediate endpoints that truly correlate with patient-centered outcomes. At Altis Labs, we’re tackling this challenge directly: developing AI-based intermediate endpoints that more accurately and earlier predict true clinical benefit. Our goal: help biopharma bring life-saving drugs to patients faster, with greater certainty. _____ FDA draft guidance: https://lnkd.in/eTGnDUEE Endpoints article: https://lnkd.in/ebqQwHtE

𝗔𝗹𝘁𝗶𝘀 𝗟𝗮𝗯𝘀: 𝗨𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝗗𝗿𝘂𝗴 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗔𝗻 𝗜𝗺𝗽𝗮𝗰𝘁 𝗦𝘁𝗼𝗿𝘆) Clinical trial failures for new treatments are common and costly, but Toronto-based Altis Labs is using AI to predict patient progression and survival, offering a new path forward. In 2023, the company raised $6M USD in seed funding to grow its team and, through partnerships with top Canadian cancer centres, validated and refined its AI models. Through DIGITAL’s Accelerating Cancer Trials with Digital Twins project, Altis Labs built a robust data foundation that enabled AstraZeneca to deploy its technology in clinical trials. In 2025, its AI, IPRO, made headlines by reanalyzing AstraZeneca’s failed 2017 MYSTIC lung cancer trial—more accurately predicting survival outcomes than traditional tumor measurements. Read the full Impact Story here: https://bit.ly/4mil56U Why their work matters: 📉 𝟰𝟭% Failure rate of Phase III clinical trials 🖼️ 𝟭𝟴𝟮𝗠+ Size of imaging dataset curated by Altis Labs 👩⚕️ 𝟰,𝟬𝟬𝟬 Number of patients involved in Phase III trials using Altis Labs’ AI models 💰 $𝟳.𝟱𝗕 Total annual cancer costs in Canada Felix Baldauf-Lenschen #MemberImpact #healthtech #healthcareinnovation #AI #digitaltechnologies #healthcare

Felix Baldauf-Lenschen, Altis' CEO, and Duoaud Shah, VP of Clinical Partnerships, had the honour of meeting with Canada's first Minister of AI, Evan Solomon. The minister was excited to learn about our work, and looks forward to following our progress. We are grateful for the support we've received from FedDev and the Government of Canada; this support helps us scale our technology and bring new therapies to patients faster. Federal Economic Development Agency for Southern Ontario | Agence fédérale de ... Government of Canada

Join us tomorrow as we discuss novel ways of measuring treatment effects in oncology clinical trials. Thank you Jayant Narang, MD. and Greg Goldmacher for the invitation to present to PINTaD group.

We’re excited to share that we have entered into a collaboration agreement with Johnson & Johnson Innovative Medicine. Trained on the industry’s most comprehensive multimodal real-world dataset, our AI models will support the evaluation of novel outcome measures that have the potential to accelerate trial timelines, reduce costs, and improve evidence generation in drug development. This collaboration reflects our commitment to accelerating clinical development with state-of-the-art AI.