ARXIV:2602.17402 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

arXiv

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities. This task can benefit from the integration of whole-slide images, bulk transcriptomics, and…

METHOD

Full abstract

Predicting survival outcomes for non-small cell lung cancer (NSCLC) patients is challenging due to the different individual prognostic features. This task can benefit from the integration of whole-slide images, bulk transcriptomics, and DNA methylation, which offer complementary views of the patient's condition at diagnosis. However, real-world clinical datasets are often incomplete, with entire modalities missing for a significant fraction of patients. State-of-the-art models rely on available data to create patient-level representations or use generative models to infer missing modalities, but they lack robustness in cases of severe missingness. We propose a Multimodal Contrastive Variational AutoEncoder (MCVAE) to address this issue: modality-specific variational encoders capture the uncertainty in each data source, and a fusion bottleneck with learned gating mechanisms is introduced to normalize the contributions from present modalities. We propose a multi-task objective that combines survival loss and reconstruction loss to regularize patient representations, along with a cross-modal contrastive loss that enforces cross-modal alignment in the latent space. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns. Extensive evaluations on the TCGA-LUAD (n=475) and TCGA-LUSC (n=446) datasets demonstrate the efficacy of our approach in predicting disease-specific survival (DSS) and its robustness to severe missingness scenarios compared to two state-of-the-art models. Finally, we bring some clarifications on multimodal integration by testing our model on all subsets of modalities, finding that integration is not always beneficial to the task.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainDevelop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.17402 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

arXiv

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainDevelop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.17402

A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback36 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities. This task can benefit from the integration of whole-slide images, bulk transcriptomics, and DNA methylation, which offer complementary views of the patient's condition at diagnosis.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Predicting survival outcomes for non-small cell lung cancer (NSCLC) patients is challenging due to the different individual prognostic features. This task can benefit from the integration of whole-slide images, bulk transcriptomics, and DNA methylation, which offer complementary views of the patient's condition at diagnosis.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. During training, we apply stochastic modality masking to improve the robustness to arbitrary missingness patterns.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

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Open full Signal Canvas

Concepts

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Methods

Materials

PDF linked

Markets

Medical AI

Competitors

not indexed

Competitive landscape

Develop a robust predictive tool for NSCLC survival outcomes using a Multimodal Contrastive Variational AutoEncoder that handles missing clinical data modalities.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.17402 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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References(36)

Accelerating Data Processing and Benchmarking of AI Models for Pathology

2025Andrew Zhang, Guillaume Jaume et al.

Vision Foundation Models for Computed Tomography

2025Suraj Pai, Ibrahim Hadzic et al.

Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data

2025Seong-Keun Yoo, C. Fitzgerald et al.

Deep Multimodal Learning with Missing Modality: A Survey

2024Renjie Wu, Hu Wang et al.

A clinical benchmark of public self-supervised pathology foundation models

2024Gabriele Campanella, Shengjia Chen et al.

A whole-slide foundation model for digital pathology from real-world data

2024Hanwen Xu, N. Usuyama et al.

Large language models and multimodal foundation models for precision oncology

2024Daniel Truhn, Jan-Niklas Eckardt et al.

Towards a general-purpose foundation model for computational pathology

2024Richard J. Chen, Tong Ding et al.

Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data

2024Baptiste Gross, Antonin Dauvin et al.

Multimodal Patient Representation Learning with Missing Modalities and Labels

2024Zhenbang Wu, Anant Dadu et al.

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence

2023Julius Keyl, Philipp Keyl et al.

XGrad: Boosting Gradient-Based Optimizers With Weight Prediction

2023Lei Guan, Dongsheng Li et al.

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

2023Guillaume Jaume, Anurag Vaidya et al.

Multimodal data fusion for cancer biomarker discovery with deep learning

2023S. Steyaert, Marija Pizurica et al.

Missing data in multi-omics integration: Recent advances through artificial intelligence

2023Javier E. Flores, Daniel M Claborne et al.

Artificial intelligence for multimodal data integration in oncology.

2022Jana Lipková, Richard J. Chen et al.

M3Care: Learning with Missing Modalities in Multimodal Healthcare Data

2022Chaohe Zhang, Xu Chu et al.

RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning

2022X. Mei, Zelong Liu et al.

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

2022R. Vanguri, Jia Luo et al.

A Survey on Deep Learning Methods for Cancer Diagnosis Using Multimodal Data Fusion

2021Chems Eddine Louahem M’Sabah, A. Bouziane et al.

Showing 20 of 36 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisThe Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation

4.0

Commercially relevant

Higher ViabilityMUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

7.0

Higher ViabilityHandling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

7.0

Higher ViabilityVLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

7.0

Higher ViabilityLongitudinal NSCLC Treatment Progression via Multimodal Generative Models

7.0

Higher ViabilityEnhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration

6.0

Conflicting

Competing ApproachLearning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC

2.0

Competing ApproachMultimodal Survival Analysis with Locally Deployable Large Language Models

5.0

Competing ApproachOn the Cone Effect and Modality Gap in Medical Vision-Language Embeddings

4.0

Competing ApproachGood Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Models

2.0

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Agent drawer

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Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.17402
Route: /paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities
Active tab: read
Artifact: a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

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API/MCP endpoints

REST paper pack API/api/v1/paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities/paper-pack
REST build passport API/api/v1/paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

Canonical ID a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities | Route /paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

/buildability/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

Watchwatch

Subject: A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

Paper ref

a-contrastive-variational-autoencoder-for-nsclc-survival-prediction-with-missing-modalities

arXiv id

2602.17402

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

dc4d784feab386553728ef02df78cbc8283b129a7d845153117312cfed99fe7a

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities

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