User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Welch, Mattea; McIntosh, Chris; McNiven, Andrea; Huang, Shao Hui; Zhang, Bei Bei; Wee, Leonard; Traverso, Alberto; Hoebers, Frank; Dekker, André; Jaffray, David A.

doi:10.1016/j.ejmp.2020.01.027

Cited by 15 publications

(14 citation statements)

References 53 publications

(53 reference statements)

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“…The idea of combining clinical, imaging, and treatment data to inform prediction is not new with pipelines developed to incorporate all aspects into model-building ( 12 ). However, each factor is usually assumed to contribute the same increase in risk across all patients, so interactions are ignored.…”

Section: Discussionmentioning

confidence: 99%

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Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

et al. 2022

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PurposeLower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location.MethodsDose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models.ResultsDose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose.ConclusionsPatients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data.

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Section: Discussionmentioning

confidence: 99%

“…Few imaging biomarker studies investigate dosimetric parameters, and vice versa, and their interactions are therefore not often described ( 12 ). Disregarding such important interactions can lead to studies incorrectly claiming a lack of association ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

et al. 2022

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“…The extraction of radiomic features from the dose distribution of the RT treatment—an approach recently called dosiomics 24 —could unveil richer information than dose‐volume histograms, typically used in TCP models. It has been demonstrated that integrating dose features with image‐based data improves the predictive capability of radiomic 25 and DL models 26 . Moreover, there are many reports showing correlation of prescribed BED with local control 27 .…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated that integrating dose features with image-based data improves the predictive capability of radiomic 25 and DL models. 26 Moreover, there are many reports showing correlation of prescribed BED with local control. 27 It was previously demonstrated that average, minimum, and maximum BED to the planning target volume (PTV) and gross tumor volume (GTV) are correlated with local control 28 and that higher BED even results in an improvement in locoregional control and survival.…”

Section: Introductionmentioning

confidence: 99%

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Avanzo

Gagliardi

Stancanello³

et al. 2021

Medical Physics

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Purpose The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non‐small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre‐treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. Materials and methods Image features, consisting of crafted radiomic features or machine‐learned features extracted using a convolutional neural network, were calculated from pre‐treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT), radiomics CT and BED (RadCT,BED), deep learning (DL) CT (DLCT), and DL CT and BED (DLCT,BED). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. Results Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75–0.85)) and AUC of 0.773 (0.688–0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649–0.829) and AUC of 0.812 (0.755–0.867). Conclusion According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

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“…Once similar patients are identified, the diagnosis, treatment, and outcome extracted from EHRs and other digital content can be ranked to give recommendations [17], e.g., by computerized clinical decision support systems (CDSS), which aid in decision-making [30]. In this way, pipelines can be designed to continuously and automatically extract information and improve the accuracy of patient outcome prediction [31].…”

Section: Artificial Intelligence In Healthcarementioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Cited by 15 publications

References 53 publications

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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