  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.
