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A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

ABSTRACT: Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's pre- and mid-treatment information to improve the prediction of grade 4 radiotherapy-induced lymphopenia. We proposed a two-input channel hybrid deep learning model to process dosimetric information using a stacked bi-directional long-short term memory structure and non-dosimetric information using a multilayer perceptron structure independently before integrating the dosimetric and non-dosimetric information for final prediction. The model was trained from 505 patients and tested in 216 patients. We compared our model with other popular predictive models, including logistic regression (with and without elastic-net regularization) random forest, support vector machines, and artificial neural network. Our hybrid deep learning model out-performed other predictive models in various evaluation metrics. It achieved the highest area under the curve at 0.831, accuracy at 0.769, F1 score at 0.631, precision at 0.670, and recall at 0.610. The hybrid deep learning model also demonstrated robustness in exploiting the value of dosimetric parameters in predictive modeling. We demonstrated that our hybrid deep learning model with a two-input channel structure, which addressed the sequential and inter-correlated nature of dosimetric parameters, could potentially improve the prediction of radiotherapy-induced lymphopenia. Our proposed deep learning framework is flexible and transferable to other related radiotherapy-induced toxicities.

SUBMITTER: Zhu C

PROVIDER: S-EPMC7501732 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Zhu Cong C Lin Steven H SH Jiang Xiaoqian X Xiang Yang Y Belal Zayne Z Jun Goo G Mohan Radhe R

Physics in medicine and biology 20200204 3

Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's pre- and mid-treatment information to improve the prediction of grade 4 radiotherapy-induced lymphopenia. We proposed a two-input channel hybrid deep learning model to process dosimetric information using a stacked bi-directional long-short term mem ...[more]

PMID: 31851954

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Project description:PurposeRadiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk.Materials and methodsWe conducted "virtual clinical trials" utilizing retrospective data for 10 intensity-modulated radiation therapy (IMRT) and 10 passively-scattered proton therapy (PSPT) esophageal cancer patients. For each patient, additional treatment plans of the modality other than the original were created employing standard-of-care (SOC) dose constraints. Predicted values of absolute lymphocyte count (ALC) nadir for all plans were estimated using a previously-developed DL model. The model also yielded the relative magnitudes of contributions of iOARs dosimetric factors to ALC nadir, which were used to compute iOARs dose-volume constraints, which were incorporated into optimization criteria to produce "IMRT-enhanced" and "intensity-modulated proton therapy (IMPT)-enhanced" plans.ResultsModel-predicted ALC nadir for the original IMRT (IMRT-SOC) and PSPT plans agreed well with actual values. IMPT-SOC showed greater immune sparing vs IMRT and PSPT. The average mean body doses were 13.10 Gy vs 7.62 Gy for IMRT-SOC vs IMPT-SOC for patients treated with IMRT-SOC; and 8.08 Gy vs 6.68 Gy for PSPT vs IMPT-SOC for patients treated with PSPT. For IMRT patients, the average predicted ALC nadir of IMRT-SOC, IMRT-enhanced, IMPT-SOC, and IMPT-enhanced was 281, 327, 351, and 392 cells/µL, respectively. For PSPT patients, the average predicted ALC nadir of PSPT, IMPT-SOC, and IMPT-enhanced was 258, 316, and 350 cells/µL, respectively. Enhanced plans achieved higher predicted ALC nadir, with an average improvement of 40.8 cells/µL (20.6%).ConclusionThe proposed DL model-guided strategy to incorporate the immune system as iOAR in IMRT and IMPT optimization has the potential for radiation-induced lymphopenia mitigation. A prospective clinical trial is planned.

Dataset Information

A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Publications

A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

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