Multidisciplinary tumor board,
simulated.

Evidence-based, structured treatment recommendations for oncologists. Clinical assessment validated by Chain-of-Verification; including insurance reimbursement checks.

See the workflow

Sample case · NSCLC Stage IIIA

Non-small cell lung cancer, T2a N2 M0. EGFR exon 19 deletion positive, PD-L1 TPS 45%. High staging confidence, high treatment confidence.

Insurance coverage status automatically checked; within reimbursement scope.

Board recommendation Osimertinib 80 mg/day — NCCN Category 1, ESMO I-A evidence level. Adjuvant initiation recommended following neoadjuvant chemotherapy.

Board preparation consumes half the time.

Physician seated at desk with confident posture

The current challenge

Multidisciplinary decision-making is one of the most valuable steps in oncology practice — yet its infrastructure runs on paper, notes, and spreadsheets in most centers. This is slow, inconsistent, and pulls the clinician's attention away from the patient.

  • Lengthy preparation — pre-board process is not standardized
  • Expertise access — multidisciplinary teams for rare tumors are limited
  • Manual reimbursement checks — slow and error-prone
  • Incomplete data tracking — biomarker and test tracking is not systematic
  • Guideline lag — NCCN/ESMO updates are slow to reach practice
  • No standardization — board decisions differ in format across centers

A board in five steps.

Every case goes through the same structured process.

00

Knowledge base preparation

The tumor type is identified; tumor-specific guidelines, biomarker matrix, and reimbursement references are activated.

01

Case summary

Patient data is structured. Missing fields are flagged back to the clinician; no assumptions are made.

02

Board members

Core participants are fixed for every case; dynamic specialists are added based on the case's specific requirements.

03

Independent assessment

Each specialist produces an independent, evidence-referenced report. Opinions are formed without consensus pressure.

04

Consensus and self-check

Reports are synthesized, conflicts are identified, and Chain-of-Verification is run.

Fifty tumor types, seven groups.

A broad spectrum from common to rare tumors.

i.Thoracic & GI

NSCLC (SCLC), colorectal, gastric/GEJ, pancreatic, hepatocellular carcinoma, esophageal.

6 types
ii.Urogenital & gynecologic

Prostate, bladder, renal cell carcinoma, ovarian, endometrial, cervical.

6 types
iii.Dermatologic & neuro-endocrine

Melanoma, head and neck, thyroid, sarcoma, glioma/GBM, meningioma, NET/NEC.

7 types
iv.Hematologic

DLBCL, Hodgkin, multiple myeloma, AML, ALL, CML, CLL.

7 types
v — vii.Rare & special

Testicular, cholangiocarcinoma, mesothelioma, vulvar, penile, and other rare tumors.

20+ types

Breast cancer with all subtypes and NSCLC with all oncogenic drivers operate as separate knowledge base modules. Molecular matching and reimbursement reference modules serve all groups.

Six engineering decisions.

Each one is a response to a concrete need we encountered in clinical practice.

  1. 01

    Chain-of-Verification

    Automated checkpoints at every processing step. Staging inconsistencies and erroneous matches are caught before the report is generated.

  2. 02

    Conditional branching

    When test results are missing, two scenarios are built: "If positive, A; if negative, B." The oncologist has a roadmap even while waiting for results.

  3. 03

    Iterative update

    When a new test result arrives, the entire report is not regenerated; only the affected sections are updated.

  4. 04

    Confidence scoring

    Staging confidence and treatment confidence are graded separately — high, moderate, or low.

  5. 05

    Insurance integration

    The reimbursement status of every recommended drug is automatically checked. "Guideline-appropriate" and "covered in this country" now sit at the same table.

  6. 06

    Modular architecture

    New tumor types, guideline updates, and knowledge modules are added without disrupting the existing structure.

The difference between general-purpose AI and a clinical system.

OncoHub  ·  General AI tools  ·  Institutional portals
FeatureOncoHubGeneral AIPortals
Insurance reimbursement integrationYesNoNo
50+ tumor type coverageYesLimitedLimited
Multi-specialist simulationYesNoNo
Chain-of-VerificationYesNoNo
Local clinical languageYesTranslationLimited
Missing data detectionYesNoNo
NCCN / ESMO referenceYesUncertainYes

Hallucination in clinical output is not an option.

Every recommendation passes through four filters.

  1. 01
    Guideline adherence

    Recommendations are sourced exclusively from NCCN, ESMO, and validated institutional guidelines. Not a single line is written without a source.

  2. 02
    Clinical validation

    The knowledge base is periodically validated by the oncology advisory board. Practical approval, not academic — grounded in real clinical use.

  3. 03
    Chain-of-Verification

    Staging, biomarker matching, and treatment decisions are each checked independently. If an inconsistency is found, the report is not published.

  4. 04
    KVKK and data security

    Patient data is encrypted and anonymized; processed within Turkey's borders. No output is stored for model training.

Elevate your board, not the burden.

OncoHub Tumor Board is designed to augment your decision-making capability. Not automation — better preparation, a cleaner starting point.

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