Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma.
Lung cancer is by far the leading cause of cancer death among both men and women. Radiation therapy is one of the main approaches to lung cancer treatment, and its planning is crucial for the therapy outcome. However, the current practice that uniformly delivers the dose does not take into account t...
| Published in: | Artificial Intelligence in Medicine Vol. 119 |
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| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
Sep2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152426611&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152426611 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Sep2021 vid: 119 pid: 1004 pub: Elsevier B.V. artinfo: ui: 152426611 152426611 NLM34531006 152426611 10.1016/j.artmed.2021.102137 NLM34531006 152426611 ppct: 1 formats: tig: atl: Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. aug: au: Tortora, Matteo Cordelli, Ermanno Sicilia, Rosa Miele, Marianna Matteucci, Paolo Iannello, Giulio Ramella, Sara Soda, Paolo affil: Unit of Computer Systems & Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, 00128, Roma, Italy sug: subj: Carcinoma, Non-Small-Cell Lung Radiotherapy Lung Neoplasms Radiotherapy Human Radiotherapy, Computer-Assisted Radiation Dosage Female Male Comparative Studies Multicenter Studies Female Male ab: Lung cancer is by far the leading cause of cancer death among both men and women. Radiation therapy is one of the main approaches to lung cancer treatment, and its planning is crucial for the therapy outcome. However, the current practice that uniformly delivers the dose does not take into account the patient-specific tumour features that may affect treatment success. Since radiation therapy is by its very nature a sequential procedure, Deep Reinforcement Learning (DRL) is a well-suited methodology to overcome this limitation. In this respect, in this work we present a DRL controller optimizing the daily dose fraction delivered to the patient on the basis of CT scans collected over time during the therapy, offering a personalized treatment not only for volume adaptation, as currently intended, but also for daily fractionation. Furthermore, this contribution introduces a virtual radiotherapy environment based on a set of ordinary differential equations modelling the tissue radiosensitivity by combining both the effect of the radiotherapy treatment and cell growth. Their parameters are estimated from CT scans routinely collected using the Particle Swarm Optimization algorithm. This permits the DRL to learn the optimal behaviour through an iterative trial and error process with the environment. We performed several experiments considering three rewards functions modelling treatment strategies with different tissue aggressiveness and two exploration strategies for the exploration-exploitation dilemma. The results show that our DRL approach can adapt to radiation therapy treatment, optimizing its behaviour according to the different reward functions and outperforming the current clinical practice. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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