Radiomics for radiation oncologists: are we ready to go?

Vaugier, L.; Ferrer, Ludovic; Mengue, Laurence; Jouglar, Emmanuel

doi:10.1259/bjro.20190046

Cited by 10 publications

(4 citation statements)

References 99 publications

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“…Up to date a large number of studies were done but implementation in clinical context is still challenging. Still radiomics is not clinically implemented since there are limitations for integrating radiomics in radiotherapy practice ( 18 ). There are advantages of using morphometric features compared to other categories of radiomics features.…”

Section: Discussionmentioning

confidence: 99%

Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy

Sherminie,

Jayatilake,

Hewavithana

et al. 2023

Front. Oncol.

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IntroductionGliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy.Methods105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated.ResultsTumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features.DiscussionNonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.

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Section: Discussionmentioning

confidence: 99%

Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy

Sherminie,

Jayatilake,

Hewavithana

et al. 2023

Front. Oncol.

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“…Local radiologic evaluation after SBRT classicaly required months after treatment, and may appear unsatisfactory in this regard [ 11 ]. Sophisticated imaging analysis based on machine-learning algorithms [ 38 ] could help accelerate the discrimination between fibrosis/pseudoprogression and recurrence in the future but is still far from being used in clinical practice [ 39 ]. Plasma ceramide variation with SBRT [ 25 ] is an early biomarker of response to SBRT as we have already shown in our ancillary study [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Radiosensitizing Chemotherapy (Irinotecan) with Stereotactic Body Radiation Therapy for the Treatment of Inoperable Liver and/or Lung Metastases of Colorectal Cancer

Vaugier

Mirabel

Racadot

et al. 2021

Cancers

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Background: Stereotactic body radiotherapy (SBRT) is a recognized treatment for colorectal cancer (CRC) metastases. We postulated that local responses could be improved by SBRT with a concomitant radiosensitizing agent (irinotecan). Methods: RADIOSTEREO-CAMPTO was a prospective multi-center phase 2 trial investigating SBRT (40–48 Gy in 4 fractions) for liver and/or lung inoperable CRC oligometastases (≤3), combined with two weekly intravenous infusions of 40 mg/m2 Irinotecan. Primary outcome was the objective local response rate as per RECIST. Secondary outcomes were early and late toxicities, EORTC QLQ-C30 quality of life, local control and overall survival. Results: Forty-four patients with 51 lesions (liver = 39, lungs = 12) were included. Median age was 69 years (46–84); 37 patients (84%) had received at least two prior chemotherapy treatments. Median follow-up was 48.9 months. One patient with two lung lesions was lost during follow-up. Assuming maximum bias hypothesis, the objective local response rate in ITT was 86.3% (44/51—95% CI: [76.8–95.7]) or 82.4% (42/51—95% CI: [71.9–92.8]). The observed local response rate was 85.7% (42/49—95% CI: [75.9–95.5]). The 1 and 2-year local (distant) progression-free survivals were 84.2% (38.4%) and 67.4% (21.3%), respectively. The 1 and 2-year overall survivals were 97.5% and 75.5%. There were no severe acute or late reactions. The EORTC questionnaire scores did not significantly worsen during or after treatment. Conclusions: SBRT with irinotecan was well tolerated with promising results despite heavily pretreated patients.

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“…Research on AI in RT has focused on standardising care and promoting efficiency in RT work practices, through safety and efficacy studies of AI technologies, applied across various body sites. New and novel AI research employs mixed methodologies to investigate domains such as the relationship between patient outcomes and use of AI, big data radiomics for planning precision and personalisation, 45 comparisons of AI technologies in RT and assessing the qualitative impact on relinquishing traditional RT planning roles to AI technology. Radiation therapist‐led and inter‐professional research into AI in radiation therapy will be central to future RT role expansion as in time, computing power and accuracy of AI technologies will replace traditional RT tasks.…”

Section: Researchmentioning

confidence: 99%

Automation and artificial intelligence in radiation therapy treatment planning

Jones,

Thompson,

Porter

et al. 2023

J of Medical Radiation Sci

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Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI‐based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher‐quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety.

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Radiomics for radiation oncologists: are we ready to go?

Cited by 10 publications

References 99 publications

Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy

Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy

Radiosensitizing Chemotherapy (Irinotecan) with Stereotactic Body Radiation Therapy for the Treatment of Inoperable Liver and/or Lung Metastases of Colorectal Cancer

Automation and artificial intelligence in radiation therapy treatment planning

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