A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Guidi, G.; Maffei, N.; Vecchi, C.; Ciarmatori, A.; Mistretta, G.M.; Gottardi, Giovanni; Meduri, Bruno; Baldazzi, G.; Bertoni, F.; Costi, T.

doi:10.1016/j.ejmp.2015.04.009

Cited by 36 publications

(34 citation statements)

References 37 publications

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“…Figure 3 is the algorithm architecture for this study. From the DVH input, clustering which classifies into data group, support vector machine (SVM) training which analyzes the parotid gland, and clinical acceptance level with test and output process are shown in Figure 3 [7]. Thus, the results suggest that the replanning for 77% patients is needed because the significant morphodosimetric changes affect them when the fourth week of treatment starts.…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…Thus, kilovoltage cone-beam computed tomography (kV-CBCT) and mega-voltage computed tomography (MVCT) combined with a linear accelerator (LINAC) permit to control patient's daily anatomical change for treatment fractions in recent radiotherapy [7]. The adaptive radiotherapy (ART) could fix the anatomical variation for the patient through the dose distribution adjustment.…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…Guidi et al [7] studied the prediction of replanning benefit using unsupervised machine learning on retrospective data considering this process and patient characteristics. Figure 3 is the algorithm architecture for this study.…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…A decision tree and its rules for their conditions whether patient characteristics about chemotherapy, cell, treatment, and sex for RT radiotherapy (reproduced from Das et al [7]). …”

Section: Decision Treementioning

confidence: 99%

“…RT, radiotherapy; LNTCP, Lyman normal tissue complication probability. Algorithm architecture for prediction using clustering and support vector machine training [7].…”

Section: Head and Neck Cancermentioning

confidence: 99%

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Predictive Solution for Radiation Toxicity Based on Big Data

Lee¹,

Kim²,

Woo³

et al. 2017

Radiotherapy

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Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients' cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system.

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Section: Head and Neck Cancermentioning

confidence: 99%

Section: Head and Neck Cancermentioning

confidence: 99%

Section: Head and Neck Cancermentioning

confidence: 99%

“…A decision tree and its rules for their conditions whether patient characteristics about chemotherapy, cell, treatment, and sex for RT radiotherapy (reproduced from Das et al [7]). …”

Section: Decision Treementioning

confidence: 99%

“…RT, radiotherapy; LNTCP, Lyman normal tissue complication probability. Algorithm architecture for prediction using clustering and support vector machine training [7].…”

Section: Head and Neck Cancermentioning

confidence: 99%

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Predictive Solution for Radiation Toxicity Based on Big Data

Lee¹,

Kim²,

Woo³

et al. 2017

Radiotherapy

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A convolutional neural network model for EPID‐based non‐transit dosimetry

Bosco

Franceries

Romain³

et al. 2023

J Applied Clin Med Phys

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Purpose To develop an alternative computational approach for EPID‐based non‐transit dosimetry using a convolutional neural network model. Method A U‐net followed by a non‐trainable layer named True Dose Modulation recovering the spatialized information was developed. The model was trained on 186 Intensity‐Modulated Radiation Therapy Step & Shot beams from 36 treatment plans of different tumor locations to convert grayscale portal images into planar absolute dose distributions. Input data were acquired from an amorphous‐Silicon Electronic Portal Image Device and a 6 MV X‐ray beam. Ground truths were computed from a conventional kernel‐based dose algorithm. The model was trained by a two‐step learning process and validated through a five‐fold cross‐validation procedure with sets of training and validation of 80% and 20%, respectively. A study regarding the dependance of the amount of training data was conducted. The performance of the model was evaluated from a quantitative analysis based the ϒ‐index, absolute and relative errors computed between the inferred dose distributions and ground truths for six square and 29 clinical beams from seven treatment plans. These results were also compared to those of an existing portal image‐to‐dose conversion algorithm. Results For the clinical beams, averages of ϒ‐index and ϒ‐passing rate (2%‐2mm > 10% Dmax) of 0.24 (±0.04) and 99.29 (±0.70)% were obtained. For the same metrics and criteria, averages of 0.31 (±0.16) and 98.83 (±2.40)% were obtained with the six square beams. Overall, the developed model performed better than the existing analytical method. The study also showed that sufficient model accuracy can be achieved with the amount of training samples used. Conclusion A deep learning‐based model was developed to convert portal images into absolute dose distributions. The accuracy obtained shows that this method has great potential for EPID‐based non‐transit dosimetry.

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Machine learning and modeling: Data, validation, communication challenges

et al. 2018

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With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.

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A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Cited by 36 publications

References 37 publications

Predictive Solution for Radiation Toxicity Based on Big Data

Predictive Solution for Radiation Toxicity Based on Big Data

A convolutional neural network model for EPID‐based non‐transit dosimetry

Machine learning and modeling: Data, validation, communication challenges

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