This record contains raw data related to article “Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation" Abstract: Purpose: To improve the workflow of Total Marrow and Lymphoid Irradiation (TMLI) by enhancing the delineation of organs-at-risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models. Materials and Methods: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients and a semi-automatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR. Results: The two DL models achieved a median Dice Similarity Coefficient (DSC) of 0.84 [0.73;0.92] and 0.84 [0.77;0.93] across the OARs. The absolute median dose (Dmedian) difference between manual and the two DL models was 2% [1%;5%] and 1% [0.2%;1%]. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmedian differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 hours the time required to complete the entire segmentation process. Conclusion: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models. Statements & Declarations Funding: This work was funded by the Italian Ministry of Health, grant AuToMI (GR-2019-12370739). Competing Interests: The authors have no conflict of interests to disclose. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by D.D., N.L., L.C., R.C.B., D.L., and P.M. The first draft of the manuscript was written by D.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethics approval: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (ID 2928, 26 January 2021). ClinicalTrials.gov identifier: NCT04976205. Consent to participate: Informed consent was obtained from all individual participants included in the study.

This record contains raw data related to article “Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation" Abstract: Purpose: To improve the workflow of Total Marrow and Lymphoid Irradiation (TMLI) by enhancing the delineation of organs-at-risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models. Materials and Methods: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients and a semi-automatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR. Results: The two DL models achieved a median Dice Similarity Coefficient (DSC) of 0.84 [0.73;0.92] and 0.84 [0.77;0.93] across the OARs. The absolute median dose (Dmedian) difference between manual and the two DL models was 2% [1%;5%] and 1% [0.2%;1%]. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmedian differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 hours the time required to complete the entire segmentation process. Conclusion: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models. Statements & Declarations Funding: This work was funded by the Italian Ministry of Health, grant AuToMI (GR-2019-12370739). Competing Interests: The authors have no conflict of interests to disclose. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by D.D., N.L., L.C., R.C.B., D.L., and P.M. The first draft of the manuscript was written by D.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethics approval: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (ID 2928, 26 January 2021). ClinicalTrials.gov identifier: NCT04976205. Consent to participate: Informed consent was obtained from all individual participants included in the study.

This record contains raw data related to article “Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process" Purpose: Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. Materials and Methods: 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model’s performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model’s sensitivity and specificity, were computed. Results: The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model’s predictions were, on average, close to the real values and provided a conservative estimation of the GPR. Conclusions: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.

This record contains raw data related to article “Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process" Purpose: Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. Materials and Methods: 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model’s performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model’s sensitivity and specificity, were computed. Results: The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model’s predictions were, on average, close to the real values and provided a conservative estimation of the GPR. Conclusions: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.

This record contains raw data related to article "Automatic Planning of the Lower-Extremities for Total Marrow Irradiation Using Volumetric Modulated Arc Therapy" Abstract Purpose: Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces to split the treatment delivery into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower-extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower-extremities plan optimization. Methods: Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower-extremities was performed automatically. Target volume parameters (CTV_J-V_98%>98%) at the junction region and several dose statistics (D_98%, D_mean, and D_2%) were compared between automatic and manual plans. The Modulation Complexity Score (MCS) was used to assess plan complexity. Results: The automatic procedure required 60-90 minutes, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J-V_98%>98%), with significant differences found at the junction region, where D_mean and D_2% increased on average by 2.4% (p<0.03) and 3.0% (p<0.02), respectively. Similar plan complexity was observed (median MCS=0.12). Since March 2022 the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days. Conclusions: The developed procedure allowed to streamline the treatment planning of TMI-TMLI, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments in new centers.

This record contains raw data related to article "Automatic Planning of the Lower-Extremities for Total Marrow Irradiation Using Volumetric Modulated Arc Therapy" Abstract Purpose: Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces to split the treatment delivery into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower-extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower-extremities plan optimization. Methods: Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower-extremities was performed automatically. Target volume parameters (CTV_J-V_98%>98%) at the junction region and several dose statistics (D_98%, D_mean, and D_2%) were compared between automatic and manual plans. The Modulation Complexity Score (MCS) was used to assess plan complexity. Results: The automatic procedure required 60-90 minutes, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J-V_98%>98%), with significant differences found at the junction region, where D_mean and D_2% increased on average by 2.4% (p<0.03) and 3.0% (p<0.02), respectively. Similar plan complexity was observed (median MCS=0.12). Since March 2022 the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days. Conclusions: The developed procedure allowed to streamline the treatment planning of TMI-TMLI, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments in new centers.