A generalized model for monitor units determination in ocular proton therapy using machine learning:A proof-of-concept study

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.</p

A generalized model for monitor units determination in ocular proton therapy using machine learning:A proof-of-concept study

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.</p

The tolerance of proton radiotherapy — preliminary results

Introduction. Because the specific proton beam dose distribution (i.e. the so-called ’Bragg curve’), proton radiotherapy ensures that the high-dose region is precisely confined to the target volume while minimizing the dose delivered to healthy tissues/critical organs surrounding the tumour or to those lying in the path of the proton beam. This method has been used for patients in Kraków since November 2016. Aim. To report the early tolerance outcomes to proton radiotherapy in patients completing their treatment just before the end of August 2017. Materials and methods. Study subjects were 47 patients who had completed their treatment before the end of August 2017 with a mean age of 41.6 years (range: 16–76, median: 40). The most frequent diagnoses were skull base tumours (22 pts. — 46.8%) and brain G1 or G2 gliomas (17 pts. — 36.2%), whereas the most frequent histological types were chordomas (17 pts. — 36.2%). Proton radiotherapy was administered by pencil beam scanning and consisted of using the intensity modulated proton therapy (IMPT) technique. The total dose given per cancer type averaged as follows: (i) 70 and 74 Gy(RBE), for respectively chodrosarcomas and chordomas, (ii) 54 Gy(RBE) for brain gliomas and (iii) 70 Gy(RBE) for paranasal sinuses tumours. Early tolerance was prospectively evaluated and measured according to the CTCAE scale, version 4.03. Results. In all, 91 side effects (SE) were recorded in 44 patients. The intensity of SEs were as following: 62 SEs (68.1%) were of grade 1 intensity, 21 SEs (23.1%) were of grade 2 and 8 SEs (8.8%) were of grade 3. The most frequently developed SEs were skin reactions (29 pts. — 61.7%) or oral/pharyngeal mucositis (20 pts. — 42.6%). Because the patient follow-up period was short, presented results only describes the early tolerance to this therapy. Our findings of mild intensities for the most early side effects, at (grades 1 or 2) are consistent with other published studies

The tolerance of proton radiotherapy — preliminary results

Introduction. Because the specific proton beam dose distribution (i.e. the so-called ’Bragg curve’), proton radiotherapy ensures that the high-dose region is precisely confined to the target volume while minimizing the dose delivered to healthy tissues/critical organs surrounding the tumour or to those lying in the path of the proton beam. This method has been used for patients in Kraków since November 2016. Aim. To report the early tolerance outcomes to proton radiotherapy in patients completing their treatment just before the end of August 2017. Materials and methods. Study subjects were 47 patients who had completed their treatment before the end of August 2017 with a mean age of 41.6 years (range: 16–76, median: 40). The most frequent diagnoses were skull base tumours (22 pts. — 46.8%) and brain G1 or G2 gliomas (17 pts. — 36.2%), whereas the most frequent histological types were chordomas (17 pts. — 36.2%). Proton radiotherapy was administered by pencil beam scanning and consisted of using the intensity modulated proton therapy (IMPT) technique. The total dose given per cancer type averaged as follows: (i) 70 and 74 Gy(RBE), for respectively chodrosarcomas and chordomas, (ii) 54 Gy(RBE) for brain gliomas and (iii) 70 Gy(RBE) for paranasal sinuses tumours. Early tolerance was prospectively evaluated and measured according to the CTCAE scale, version 4.03. Results. In all, 91 side effects (SE) were recorded in 44 patients. The intensity of SEs were as following: 62 SEs (68.1%) were of grade 1 intensity, 21 SEs (23.1%) were of grade 2 and 8 SEs (8.8%) were of grade 3. The most frequently developed SEs were skin reactions (29 pts. — 61.7%) or oral/pharyngeal mucositis (20 pts. — 42.6%). Because the patient follow-up period was short, presented results only describes the early tolerance to this therapy. Our findings of mild intensities for the most early side effects, at (grades 1 or 2) are consistent with other published studies.

Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy

Purpose: Experimental assessment of inter-centre variation and absolute accuracy of stopping-power ratio (SPR) prediction within 17 particle therapy centres of the European Particle Therapy Network. Material and methods: A head and body phantom with seventeen tissue-equivalent materials were scanned consecutively at the participating centres using their individual clinical CT scan protocol and translated into SPR with their in-house CT-number-to-SPR conversion. Inter-centre variation and absolute accuracy in SPR prediction were quantified for three tissue groups: lung, soft tissues and bones. The integral effect on range prediction for typical clinical beams traversing different tissues was determined for representative beam paths for the treatment of primary brain tumours as well as lung and prostate cancer. Results: An inter-centre variation in SPR prediction (2 sigma) of 8.7%, 6.3% and 1.5% relative to water was determined for bone, lung and soft-tissue surrogates in the head setup, respectively. Slightly smaller variations were observed in the body phantom (6.2%, 3.1%, 1.3%). This translated into inter-centre variation of integral range prediction (2 sigma) of 2.9%, 2.6% and 1.3% for typical beam paths of prostate-, lung-and primary brain-tumour treatments, respectively. The absolute error in range exceeded 2% in every fourth participating centre. The consideration of beam hardening and the execution of an independent HLUT validation had a positive effect, on average. Conclusion: The large inter-centre variations in SPR and range prediction justify the currently clinically used margins accounting for range uncertainty, which are of the same magnitude as the inter-centre variation. This study underlines the necessity of higher standardisation in CT-number-to-SPR conversion. (C) 2021 The Authors. Published by Elsevier B.V

Profile of European proton and carbon ion therapy centers assessed by the EORTC facility questionnaire.

BACKGROUND We performed a survey using the modified EORTC Facility questionnaire (pFQ) to evaluate the human, technical and organizational resources of particle centers in Europe. MATERIAL AND METHODS The modified pFQ consisted of 235 questions distributed in 11 sections accessible on line on an EORTC server. Fifteen centers from 8 countries completed the pFQ between May 2015 and December 2015. RESULTS The average number of patients treated per year and per particle center was 221 (range, 40-557). The majority (66.7%) of centers had pencil beam or raster scanning capability. Four (27%) centers were dedicated to eye treatment only. An increase in the patients-health professional FTE ratio was observed for eye tumor only centers when compared to other centers. All centers treated routinely chordomas/chondrosarcomas, brain tumors and sarcomas but rarely breast cancer. The majority of centers treated pediatric cases with particles. Only a minority of the queried institutions treated non-static targets. CONCLUSIONS As the number of particle centers coming online will increase, the experience with this treatment modality will rise in Europe. Children can currently be treated in these facilities in a majority of cases. The majority of these centers provide state of the art particle beam therapy