Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction.

IF 2 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Applied Clinical Medical Physics Pub Date : 2025-01-27 DOI:10.1002/acm2.14596

Rupesh Ghimire, Lance Moore, Daniela Branco, Dominique L Rash, Jyoti S Mayadev, Xenia Ray

{"title":"Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction.","authors":"Rupesh Ghimire, Lance Moore, Daniela Branco, Dominique L Rash, Jyoti S Mayadev, Xenia Ray","doi":"10.1002/acm2.14596","DOIUrl":null,"url":null,"abstract":"Purpose: Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.Methods: For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (InitialSOC) and a reduced margin initial plan (InitialART) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (DailySOC and DailyART) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit ( <math> <semantics><mrow><mi>Δ</mi> <mi>D</mi> <mi>a</mi> <mi>i</mi> <mi>l</mi> <mi>y</mi></mrow> <annotation>${{\\Delta}}Daily$</annotation></semantics> </math> = DailySOC-DailyART) versus initial plan differences ( <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi></mrow> <annotation>${{\\Delta}}Initial$</annotation></semantics> </math> = InitialSOC-InitialART) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences ( <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi></mrow> <annotation>${{\\Delta}}Initial$</annotation></semantics> </math> ) of <math> <semantics><mrow><mi>B</mi> <mi>o</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mspace></mspace> <msub><mi>V</mi> <mrow><mn>40</mn> <mi>G</mi> <mi>y</mi></mrow> </msub> </mrow> <annotation>$Bowel\\ {{V}_{40Gy}}$</annotation></semantics> </math> (cc), <math> <semantics><mrow><mi>B</mi> <mi>l</mi> <mi>a</mi> <mi>d</mi> <mi>d</mi> <mi>e</mi> <mi>r</mi> <mspace></mspace> <msub><mi>D</mi> <mrow><mn>50</mn> <mo>%</mo></mrow> </msub> </mrow> <annotation>$Bladder\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation></semantics> </math> (Gy), and <math> <semantics><mrow><mi>R</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> <mi>u</mi> <mi>m</mi> <mspace></mspace> <msub><mi>D</mi> <mrow><mn>50</mn> <mo>%</mo></mrow> </msub> </mrow> <annotation>$Rectum\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation></semantics> </math> (Gy). Anatomy (intact uterus or post-hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high-benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave-one-out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge-based plans for the ΔInitial plans ( <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <msub><mi>l</mi> <mrow><mi>R</mi> <mi>P</mi></mrow> </msub> </mrow> <annotation>${{\\Delta}}Initia{{l}_{RP}}$</annotation></semantics> </math> ) and repeated the analysis.Results: In both <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <msub><mi>l</mi> <mrow><mi>O</mi> <mi>r</mi> <mi>i</mi> <mi>g</mi></mrow> </msub> </mrow> <annotation>${{\\Delta}}Initia{{l}_{Orig}}$</annotation></semantics> </math> and <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <msub><mi>l</mi> <mrow><mi>R</mi> <mi>P</mi></mrow> </msub> </mrow> <annotation>${{\\Delta}}Initia{{l}_{RP}}$</annotation></semantics> </math> our multivariate analysis showed low R2 values 0.34-0.52 versus 0.14-0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., <math> <semantics><mrow><mi>Δ</mi> <mi>I</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi></mrow> <annotation>${{\\Delta}}Initial$</annotation></semantics> </math> Bowel (V40 Gy), p < 1e-05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were <math> <semantics><mrow><mi>B</mi> <mi>o</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mspace></mspace> <msub><mi>V</mi> <mrow><mn>40</mn> <mi>G</mi> <mi>y</mi></mrow> </msub> </mrow> <annotation>$Bowel\\ {{V}_{40Gy}}$</annotation></semantics> </math> (cc), <math> <semantics><mrow><mi>B</mi> <mi>l</mi> <mi>a</mi> <mi>d</mi> <mi>d</mi> <mi>e</mi> <mi>r</mi> <mspace></mspace> <msub><mi>D</mi> <mrow><mn>50</mn> <mo>%</mo></mrow> </msub> </mrow> <annotation>$Bladder\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation></semantics> </math> (Gy), <math> <semantics><mrow><mi>D</mi> <mi>o</mi> <mi>s</mi> <mi>e</mi> <mi>T</mi> <mi>y</mi> <mi>p</mi> <mi>e</mi></mrow> <annotation>$DoseType$</annotation></semantics> </math> , and <math> <semantics><mrow><mi>S</mi> <mi>I</mi> <mi>B</mi> <mi>D</mi> <mi>o</mi> <mi>s</mi> <mi>e</mi></mrow> <annotation>$SIBDose$</annotation></semantics> </math> prescription. The models for original and knowledge-based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.Conclusion: This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14596"},"PeriodicalIF":2.0000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Clinical Medical Physics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/acm2.14596","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.

Methods: For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (Initial_SOC) and a reduced margin initial plan (Initial_ART) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily_SOC and Daily_ART) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit ( $Δ D a i l y$ = Daily_SOC-Daily_ART) versus initial plan differences ( $Δ I n i t i a l$ = Initial_SOC-Initial_ART) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences ( $Δ I n i t i a l$ ) of $B o w e l V_{40 G y}$ (cc), $B l a d d e r D_{50 %}$ (Gy), and $R e c t u m D_{50 %}$ (Gy). Anatomy (intact uterus or post-hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high-benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave-one-out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge-based plans for the ΔInitial plans ( $Δ I n i t i a l_{R P}$ ) and repeated the analysis.

Results: In both $Δ I n i t i a l_{O r i g}$ and $Δ I n i t i a l_{R P}$ our multivariate analysis showed low R² values 0.34-0.52 versus 0.14-0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., $Δ I n i t i a l$ Bowel (V40 Gy), p < 1e-05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were $B o w e l V_{40 G y}$ (cc), $B l a d d e r D_{50 %}$ (Gy), $D o s e T y p e$ , and $S I B D o s e$ prescription. The models for original and knowledge-based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.

Conclusion: This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

求助全文

约1分钟内获得全文去求助

来源期刊

Journal of Applied Clinical Medical Physics 医学-核医学

CiteScore

3.60

自引率

19.00%

发文量

331

审稿时长

3 months

期刊介绍： Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic