# Multimodal Learning for Non-small Cell Lung Cancer Prognosis

arXiv: https://arxiv.org/abs/2211.03280 · HTML: https://mlsanity.com/paper/2211.03280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Smart Reply: Automated Response Suggestion for Email](https://mlsanity.com/paper/1606.04870/) (2016)
- [Learning Semantic Textual Similarity from Conversations](https://mlsanity.com/paper/1804.07754/) (2018)
- [DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis](https://mlsanity.com/paper/2106.06744/) (2021)
- [Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations](https://mlsanity.com/paper/1307.6417/) (2013)

## Built on by (most cited first, 1 of 1)

- [Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2601.10386/) (2026)

## Abstract (arXiv)

This paper focuses on the task of survival time analysis for lung cancer. Although much progress has been made in this problem in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning-based survival time analyses for lung cancer are mostly based on textual clinical information such as staging, age, histology, etc. Unlike existing methods that predicting on the single modality, we observe that a human clinician usually takes multimodal data such as text clinical data and visual scans to estimate survival time. Motivated by this, in this work, we contribute a smart cross-modality network for survival analysis network named Lite-ProSENet that simulates a human's manner of decision making. Extensive experiments were conducted using data from 422 NSCLC patients from The Cancer Imaging Archive (TCIA). The results show that our Lite-ProSENet outperforms favorably again all comparison methods and achieves the new state of the art with the 89.3% on concordance. The code will be made publicly available.