What does this data show?

A verified, classified map of the #DCLung26 conversation: 132 accounts from curated posts, including 48 physicians — 6 US physicians and 42 international colleagues from 12 countries (UK, Ireland, Switzerland, Philippines lead). Institutional clusters are visible in the conference conversation: MedStar Georgetown University Hospital / Georgetown Lombardi (3), MD Anderson Cancer Center (3), Dana-Farber Cancer Institute (2), The Ohio State University Comprehensive Cancer Center (The James) (2). Trial interest concentrates around NeoADAURA (16 physicians), IMforte (10 physicians), TRIDENT-1 (10 physicians). 5 patient advocates and advocacy organizations are active in the same conversation — and the bot and persona accounts that inflate raw follower metrics were identified and quarantined, so every physician here is bio-verified.

How can a KOL learn from this report?

Benchmark your own #DCLung26 presence against the 48 physicians mapped here: are your trials of interest among the crowded conversations (NeoADAURA, IMforte, TRIDENT-1) or the quiet ones where a single voice still stands out? The category and region breakdowns show which patient advocacy voices and international colleagues were active in your space, and the trial section names the physicians - with their institutions - engaged with each trial, colleagues who were in the room in Washington, D.C.

What does a worked example of using this data look like?

An MSL following up after DC Lung 2026: MedStar Georgetown University Hospital / Georgetown Lombardi alone had 3 physicians in the #DCLung26 conversation. Cross-reference the trial section — NeoADAURA (16 physicians), IMforte (10 physicians), TRIDENT-1 (10 physicians) are the crowded conversations — and the follow-up plan writes itself: which physicians at which institutions engaged with which trials during the meeting, before a single follow-up is booked. A KOL can run the same play in reverse: find your institution's cluster, see which trials your colleagues converged on, and position your own commentary where the map shows white space.

How can medical affairs and pharma teams use this report?

As post-meeting follow-up planning grounded in this page's own numbers: the category and region breakdowns surface the 6 verified US physicians and their international colleagues, each with institution and trial interests; the classification keeps engagement lists clean by separating treating physicians from the 8 media accounts and industry voices; and the trial-interest chips let field teams match MSL briefings to the trials a physician already follows. KOL identification tells you who matters; mapping tells you how the community organizes around institutions and trials — which is what engagement sequencing actually needs. Influence-mapping practice guides note this also reduces redundant outreach: teams see who is already saturated and who is unengaged.

Why is social influence mapping important in oncology?

Oncology standards change with each congress readout, and physicians increasingly learn about readouts from each other online — surveys report over 70% of HCPs use digital channels weekly for medical information, and in a LiveWorld–Sermo survey of 206 US physicians, 41% said social media influences prescribing behavior. A conference map matters because meeting days are when oncology opinion forms: this dataset shows the communities that organized around DC Lung 2026 — MedStar Georgetown University Hospital / Georgetown Lombardi and MD Anderson Cancer Center clusters, 5 active patient advocacy organizations, and NeoADAURA-focused physician groups.

What is the difference between a KOL and a Digital Opinion Leader?

A traditional KOL earns influence through trials, publications, and guideline work; a Digital Opinion Leader earns it through digital reach — posts, threads, podcasts, and amplification. Many physicians on this page are both: trial investigators whose conference posts reach tens of thousands. This report measures the digital half directly and pairs it with verified identity, institution, and trial interests so the two kinds of influence can be read together.

How can I use this report with my favorite AI tool?

This page is built to be read by AI assistants (ChatGPT, Gemini, Claude, Perplexity). Paste the URL into your tool and ask questions this page can actually answer: “Which institutions had the most physicians in the #DCLung26 conversation?”, “Which trials were DC Lung 2026 physicians most engaged with?”, “Which physicians engaged with NeoADAURA at the meeting?”, “List the patient advocacy organizations active around DC Lung 2026.” For who-influences-whom questions (a KOL’s amplifier network, which US physicians amplify a given voice), use the companion DC Lung 2026 influencer network. Cite kolpulse.com when you share the answers.