# Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection

arXiv: https://arxiv.org/abs/1909.05321 · HTML: https://mlsanity.com/paper/1909.05321/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes](https://mlsanity.com/paper/2109.00532/) (2021)
- [A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study](https://mlsanity.com/paper/2303.10789/) (2023)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [MARL: Multimodal Attentional Representation Learning for Disease Prediction](https://mlsanity.com/paper/2105.00310/) (2021)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data](https://mlsanity.com/paper/2502.07836/) (2025)
- [CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data](https://mlsanity.com/paper/2104.03739/) (2021)
- [Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging](https://mlsanity.com/paper/2408.09367/) (2024)

## Abstract (arXiv)

The field of lung nodule detection and cancer prediction has been rapidly developing with the support of large public data archives. Previous studies have largely focused on cross-sectional (single) CT data. Herein, we consider longitudinal data. The Long Short-Term Memory (LSTM) model addresses learning with regularly spaced time points (i.e., equal temporal intervals). However, clinical imaging follows patient needs with often heterogeneous, irregular acquisitions. To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions. The DLSTM includes a Temporal Emphasis Model (TEM) that enables learning across regularly and irregularly sampled intervals. Briefly, (1) the time intervals between longitudinal scans are modeled explicitly, (2) temporally adjustable forget and input gates are introduced for irregular temporal sampling; and (3) the latest longitudinal scan has an additional emphasis term. We evaluate the DLSTM framework in three datasets including simulated data, 1794 National Lung Screening Trial (NLST) scans, and 1420 clinically acquired data with heterogeneous and irregular temporal accession. The experiments on the first two datasets demonstrate that our method achieves competitive performance on both simulated and regularly sampled datasets (e.g. improve LSTM from 0.6785 to 0.7085 on F1 score in NLST). In external validation of clinically and irregularly acquired data, the benchmarks achieved 0.8350 (CNN feature) and 0.8380 (LSTM) on the area under the ROC curve (AUC) score, while the proposed DLSTM achieves 0.8905.