Multimodal AI · Precision Oncology

Transforming cancer care with AI-powered precision oncology

DocLane predicts clinically relevant mutations directly from routine H&E pathology images, then fuses them with radiology, genomics and clinical history into one decision-support layer - built and validated for Indian patients.

Explore the validation study See the technology
4–6 weeks → 1 daygenomic turnaround target
Prov-GigaPathfoundation model backbone
Tier-1 to Tier-3Indian hospital cohorts
The diagnostic bottleneck

Precision oncology is slow to reach the patient who needs it

Molecular profiling exists — but the path from biopsy to an actionable, mutation-informed treatment plan is long, costly and concentrated in a handful of metro labs. DocLane compresses that path without replacing the pathologist or the sequencing lab.

Current pathway

Standard molecular workup
  1. Biopsy collectedTissue sample taken from patient
  2. Histopathology reviewManual slide reading by pathologist
  3. NGS sent outSequencing at a centralized lab
  4. 4–6 week waitTurnaround before results return
  5. Treatment beginsTherapy selected once report arrives
SlowExpensiveMetro-concentrated

DocLane-assisted pathway

AI-augmented workup
  1. Biopsy collectedSame clinical starting point
  2. Digital pathology slideRoutine H&E slide is scanned
  3. AI mutation predictionProv-GigaPath-based model scores the slide
  4. Clinical decision supportOncologist reviews AI-assisted insight
  5. Targeted therapySequencing prioritized, therapy accelerated
Same-day insightLower cost per caseDeployable Tier-2/3
The Hypothesis

Can tumour morphology predict genomics?

Specific somatic mutations alter tumour cellular architecture, glandular patterns, nuclear morphology and stromal composition in measurable ways.

Deep learning models trained on thousands of H&E whole-slide images can learn these subtle morphological correlates and predict the underlying genomic alteration — without additional molecular testing.

See how the pipeline works
Why validation on Indian cohorts

A model trained on Western data doesn't automatically generalize to Indian patients

Prov-GigaPath and comparable pathology foundation models are trained predominantly on Western cohorts. Genetic architecture, disease biology, tissue-processing protocols and scanner characteristics all differ in Indian clinical settings — which is exactly what our validation study is built to test.

Population-specific biology

Tumor genomics, mutation prevalence and disease presentation in Indian cancer cohorts differ meaningfully from the Western datasets most foundation models are trained on.

Lab & scanner variability

Staining protocols, tissue processing and digital scanner hardware vary widely across Indian pathology labs, all of which can shift how an AI model reads a slide.

Access beyond metros

Genomic testing is concentrated in a few metropolitan labs. Independent, India-specific validation is the prerequisite for responsibly extending AI-assisted insight to Tier-2 and Tier-3 hospitals.

Multimodal AI platform

One engine, four data sources, one-day answers

DocLane converts clinical history, imaging, pathology and genomics into mutation-informed treatment intelligence — all in one day.

AI inference pipeline

From whole-slide image to clinical decision support

Built on a self-supervised pathology foundation model trained on real-world histopathology tiles, fine-tuned toward Indian whole-slide image and NGS cohorts.

  1. Whole slide image

    Routine H&E slide digitized at the partner hospital.

  2. Tiling

    WSI is divided into thousands of analyzable tiles.

  3. Foundation model

    DocLane encodes morphological representations.

  4. Feature extraction

    Tile-level features aggregated to slide level.

  5. Mutation prediction

    Model scores likely genomic alterations and TMB.

  6. Clinical decision support

    Oncologist reviews AI-assisted output alongside the chart.

Phase 1 validation snapshot

A phased, hospital-partnered validation roadmap

Phase 1 is a retrospective and prospective feasibility study testing whether Prov-GigaPath-derived features predict known genomic mutations from H&E slides already collected at partner sites — benchmarked directly against existing NGS results.

  1. HOSPITAL
    PARTNERS

    Recruit cancer centres & oncologists

    Retrospective, de-identified cohorts from collaborating hospitals with matched H&E slides and NGS reports.

  2. DIGITIZED
    SLIDES

    Whole-slide digitization

    Existing H&E slides scanned to WSI format and quality-checked for tiling.

  3. AI
    VALIDATION

    Model inference on Indian dataset

    Prov-GigaPath-based predictions generated and compared against ground-truth NGS results.

  4. CLINICAL
    BENCHMARK

    Benchmarking & multicentre expansion

    Performance benchmarked with oncologist review, informing a future prospective, multicentre study.

Clinical applications

What the AI engine outputs to the care team

Mutation risk prediction

Likelihood of clinically relevant genomic alterations from routine H&E imaging.

TMB & biomarker report

Tumor mutational burden and biomarker estimates to inform stratification.

Targeted therapy recommendation

AI-assisted suggestions to support, not replace, oncologist decision-making.

Resistance mechanism alert

Flags patterns associated with potential treatment resistance for review.

Research-stage impact targets

What we're building toward

These are the targets guiding Phase 1 — presented as goals of an early-stage research program.

4–6 weeks
Current genomic turnaround
1 Day
AI-assisted mutation insight target
%
Early model accuracy (research stage)
+
Planned Indian validation cases
Tier-2 & 3
Future hospital expansion