Use of radiomics and dosiomics to identify predictors of radiation induced lung injury

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Abstract

BACKGROUND: Radiomics is a machine learning based technology that extracts, analyzes, and interprets quantitative features from digital medical images. In recent years, dosiomics has become an increasingly common term in the literature to describe a new radiomics method. Dosiomics is a texture analysis method for evaluating radiotherapy dose distribution patterns. Most of the published research in dosiomics evaluates its use in predicting radiation induced lung injury.

AIM: The aim of the study was to identify predictors (biomarkers) of radiation induced lung injury using texture analysis of computed tomography (CT) images of lungs and chest soft tissues using radiomics and dosiomics.

MATERIALS AND METHODS: The study used data from 36 women with breast cancer who received postoperative conformal radiation therapy. Retrospectively, the patients were divided into two groups according to the severity of post radiation lung lesions. 3D Slicer was used to evaluate CT results of all patients obtained during radiation treatment planning and radiation dose distribution patterns. The software was able to unload radiomic and dosiomic features from regions of interest. The regions of interest included chest soft tissue and lung areas on the irradiated side where the dose burden exceeded 3 and 10 Gy.

RESULTS: The first group included 13 patients with minimal radiation induced lung lesions, and the second group included 23 patients with post radiation pneumofibrosis. In the lung area on the side irradiated with more than 3 Gy, statistically significant differences between the patient groups were obtained for three radiomic features and one dosiomic feature. In the lung area on the side irradiated with more than 10 Gy, statistically significant differences were obtained for 12 radiomic features and 1 dosiomic feature. In the area of chest soft tissues on the irradiated side, significant differences were obtained for 18 radiomic features and 4 dosiomic features.

CONCLUSIONS: As a result, a number of radiomic and dosiomic features were identified which were statistically different in patients with minimal lesions and pulmonary pneumofibrosis following radiation therapy for breast cancer. Based on texture analysis, predictors (biomarkers) were identified to predict post radiation lung injury and identify higher risk patients.

About the authors

Nikolay V. Nudnov

Russian Scientific Center of Roentgenoradiology; Russian Medical Academy of Continuous Professional Education; Peoples’ Friendship University of Russia

Author for correspondence.
Email: nudnov@rncrr.ru
ORCID iD: 0000-0001-5994-0468
SPIN-code: 3018-2527

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow; Moscow; Moscow

Vladimir M. Sotnikov

Russian Scientific Center of Roentgenoradiology

Email: vmsotnikov@mail.ru
ORCID iD: 0000-0003-0498-314X
SPIN-code: 3845-0154

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow

Mikhail E. Ivannikov

Russian Scientific Center of Roentgenoradiology

Email: ivannikovmichail@gmail.com
ORCID iD: 0009-0007-0407-0953
SPIN-code: 3419-2977

MD

Russian Federation, Moscow

Elina S.-A. Shakhvalieva

Russian Scientific Center of Roentgenoradiology

Email: shelina9558@gmail.com
ORCID iD: 0009-0000-7535-8523

MD

Russian Federation, Moscow

Aleksandr A. Borisov

Russian Scientific Center of Roentgenoradiology

Email: aleksandrborisov10650@gmail.com
ORCID iD: 0000-0003-4036-5883
SPIN-code: 4294-4736

MD

Russian Federation, Moscow

Vasiliy V. Ledenev

Central Clinical Military Hospital

Email: Ledenevvv007@gmail.com
ORCID iD: 0000-0002-2856-2107
SPIN-code: 2791-0329

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Aleksei Yu. Smyslov

Russian Scientific Center of Roentgenoradiology

Email: smyslov.ay@gmail.com
ORCID iD: 0000-0002-6409-6756
SPIN-code: 9341-0037

Cand. Sci. (Engineering)

Russian Federation, Moscow

Alina V. Ananina

Russian Scientific Center of Roentgenoradiology

Email: vastruhina.a.v@yandex.ru
ORCID iD: 0009-0002-4562-9729
SPIN-code: 9699-7690
Russian Federation, Moscow

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2. Fig. 1. Computed tomography of the chest organs of patients 6 months after radiation therapy: a — minimal post—radiation changes in the left lung; b - pronounced post-radiation pneumofibrosis in the right lung.

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