PMID- 33161012
OWN - NLM
STAT- MEDLINE
DCOM- 20210423
LR  - 20210423
IS  - 1879-0887 (Electronic)
IS  - 0167-8140 (Linking)
VI  - 155
DP  - 2021 Feb
TI  - Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy 
      for lung cancer.
PG  - 144-150
LID - S0167-8140(20)30887-2 [pii]
LID - 10.1016/j.radonc.2020.10.040 [doi]
AB  - PURPOSE: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients 
      with locally advanced lung cancer non accessible for surgery. However, current 
      toxicity prediction models rely on clinical and dose volume histograms (DVHs) and 
      remain unsufficient. The goal of this work is to investigate the added predictive 
      value of the radiomics approach applied to dose maps regarding acute and late 
      toxicities in both the lungs and esophagus. METHODS: Acute and late toxicities 
      scored using the CTCAE v4.0 were retrospectively collected on patients treated 
      with RT in our institution. Radiomic features were extracted from 3D dose maps 
      considering Gy values as grey-levels in images. DVH and usual clinical factors 
      were also considered. Three toxicity prediction models (clinical only, 
      clinical + DVH and combined, i.e., including clinical + DVH + radiomics) were 
      incrementally trained using a neural network on 70% of the patients for 
      prediction of grade >/=2 acute and late pulmonary toxicities (APT/LPT) and grade >/=2 
      acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal 
      cut-off values were determined based on the Youden Index. The trained models were 
      then evaluated in the remaining 30% of patients using balanced accuracy (BAcc). 
      RESULTS: 167 patients were treated from 2015 to 2018: 78% non small-cell lung 
      cancers, 14% small-cell lung cancers and 8% other histology with a median age at 
      treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT 
      and AET. In the training set (n = 117), the corresponding BAcc for clinical 
      only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 
      0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained 
      a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72. 
      CONCLUSION: In patients with a lung cancer treated with RT, radiomic features 
      extracted from 3D dose maps seem to surpass usual models based on clinical 
      factors and DVHs for the prediction of APT and LPT.
CI  - Copyright (c) 2020. Published by Elsevier B.V.
FAU - Bourbonne, V
AU  - Bourbonne V
AD  - Radiation Oncology Department, University Hospital, Brest, France; LaTIM, UMR 
      1101, INSERM, Univ Brest, Brest, France. Electronic address: 
      vincent.bourbonne@chu-brest.fr.
FAU - Da-Ano, R
AU  - Da-Ano R
AD  - LaTIM, UMR 1101, INSERM, Univ Brest, Brest, France.
FAU - Jaouen, V
AU  - Jaouen V
AD  - LaTIM, UMR 1101, INSERM, Univ Brest, Brest, France.
FAU - Lucia, F
AU  - Lucia F
AD  - Radiation Oncology Department, University Hospital, Brest, France; LaTIM, UMR 
      1101, INSERM, Univ Brest, Brest, France.
FAU - Dissaux, G
AU  - Dissaux G
AD  - Radiation Oncology Department, University Hospital, Brest, France; LaTIM, UMR 
      1101, INSERM, Univ Brest, Brest, France.
FAU - Bert, J
AU  - Bert J
AD  - LaTIM, UMR 1101, INSERM, Univ Brest, Brest, France.
FAU - Pradier, O
AU  - Pradier O
AD  - Radiation Oncology Department, University Hospital, Brest, France; LaTIM, UMR 
      1101, INSERM, Univ Brest, Brest, France.
FAU - Visvikis, D
AU  - Visvikis D
AD  - LaTIM, UMR 1101, INSERM, Univ Brest, Brest, France.
FAU - Hatt, M
AU  - Hatt M
AD  - LaTIM, UMR 1101, INSERM, Univ Brest, Brest, France.
FAU - Schick, U
AU  - Schick U
AD  - Radiation Oncology Department, University Hospital, Brest, France; LaTIM, UMR 
      1101, INSERM, Univ Brest, Brest, France.
LA  - eng
PT  - Journal Article
DEP - 20201106
PL  - Ireland
TA  - Radiother Oncol
JT  - Radiotherapy and oncology : journal of the European Society for Therapeutic 
      Radiology and Oncology
JID - 8407192
SB  - IM
MH  - *Carcinoma, Non-Small-Cell Lung
MH  - Esophagus
MH  - Humans
MH  - *Lung Neoplasms/diagnostic imaging/radiotherapy
MH  - Radiotherapy Dosage
MH  - Retrospective Studies
OTO - NOTNLM
OT  - Dose spatial distribution
OT  - Lung cancer
OT  - Radiomics
OT  - Toxicities prediction
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2020/11/09 06:00
MHDA- 2021/04/24 06:00
CRDT- 2020/11/08 20:27
PHST- 2020/06/25 00:00 [received]
PHST- 2020/10/28 00:00 [revised]
PHST- 2020/10/29 00:00 [accepted]
PHST- 2020/11/09 06:00 [pubmed]
PHST- 2021/04/24 06:00 [medline]
PHST- 2020/11/08 20:27 [entrez]
AID - S0167-8140(20)30887-2 [pii]
AID - 10.1016/j.radonc.2020.10.040 [doi]
PST - ppublish
SO  - Radiother Oncol. 2021 Feb;155:144-150. doi: 10.1016/j.radonc.2020.10.040. Epub 
      2020 Nov 6.