有或没有基于知识的剂量预测的妇科癌症患者每日适应性放射治疗的剂量学建模益处。

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

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Daily doses corresponding to standard and reduced margins (Daily<sub>SOC</sub> and Daily<sub>ART</sub>) 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 (<span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>D</mi>\n <mi>a</mi>\n <mi>i</mi>\n <mi>l</mi>\n <mi>y</mi>\n </mrow>\n <annotation>${{\\Delta}}Daily$</annotation>\n </semantics></math>= Daily<sub>SOC</sub>-Daily<sub>ART</sub>) versus initial plan differences (<span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <mi>l</mi>\n </mrow>\n <annotation>${{\\Delta}}Initial$</annotation>\n </semantics></math>= Initial<sub>SOC</sub>–Initial<sub>ART</sub>) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences (<span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <mi>l</mi>\n </mrow>\n <annotation>${{\\Delta}}Initial$</annotation>\n </semantics></math>) of <span></span><math>\n <semantics>\n <mrow>\n <mi>B</mi>\n <mi>o</mi>\n <mi>w</mi>\n <mi>e</mi>\n <mi>l</mi>\n <mspace></mspace>\n <msub>\n <mi>V</mi>\n <mrow>\n <mn>40</mn>\n <mi>G</mi>\n <mi>y</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>$Bowel\\ {{V}_{40Gy}}$</annotation>\n </semantics></math> (cc), <span></span><math>\n <semantics>\n <mrow>\n <mi>B</mi>\n <mi>l</mi>\n <mi>a</mi>\n <mi>d</mi>\n <mi>d</mi>\n <mi>e</mi>\n <mi>r</mi>\n <mspace></mspace>\n <msub>\n <mi>D</mi>\n <mrow>\n <mn>50</mn>\n <mo>%</mo>\n </mrow>\n </msub>\n </mrow>\n <annotation>$Bladder\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation>\n </semantics></math> (Gy), and <span></span><math>\n <semantics>\n <mrow>\n <mi>R</mi>\n <mi>e</mi>\n <mi>c</mi>\n <mi>t</mi>\n <mi>u</mi>\n <mi>m</mi>\n <mspace></mspace>\n <msub>\n <mi>D</mi>\n <mrow>\n <mn>50</mn>\n <mo>%</mo>\n </mrow>\n </msub>\n </mrow>\n <annotation>$Rectum\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation>\n </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 (<span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <msub>\n <mi>l</mi>\n <mrow>\n <mi>R</mi>\n <mi>P</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>${{\\Delta}}Initia{{l}_{RP}}$</annotation>\n </semantics></math>) and repeated the analysis.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>In both <span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <msub>\n <mi>l</mi>\n <mrow>\n <mi>O</mi>\n <mi>r</mi>\n <mi>i</mi>\n <mi>g</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>${{\\Delta}}Initia{{l}_{Orig}}$</annotation>\n </semantics></math> and <span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <msub>\n <mi>l</mi>\n <mrow>\n <mi>R</mi>\n <mi>P</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>${{\\Delta}}Initia{{l}_{RP}}$</annotation>\n </semantics></math> our multivariate analysis showed low <i>R</i><sup>2</sup> 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., <span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <mi>I</mi>\n <mi>n</mi>\n <mi>i</mi>\n <mi>t</mi>\n <mi>i</mi>\n <mi>a</mi>\n <mi>l</mi>\n </mrow>\n <annotation>${{\\Delta}}Initial$</annotation>\n </semantics></math> Bowel (V40 Gy), <i>p</i> < 1e−05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were <span></span><math>\n <semantics>\n <mrow>\n <mi>B</mi>\n <mi>o</mi>\n <mi>w</mi>\n <mi>e</mi>\n <mi>l</mi>\n <mspace></mspace>\n <msub>\n <mi>V</mi>\n <mrow>\n <mn>40</mn>\n <mi>G</mi>\n <mi>y</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>$Bowel\\ {{V}_{40Gy}}$</annotation>\n </semantics></math> (cc), <span></span><math>\n <semantics>\n <mrow>\n <mi>B</mi>\n <mi>l</mi>\n <mi>a</mi>\n <mi>d</mi>\n <mi>d</mi>\n <mi>e</mi>\n <mi>r</mi>\n <mspace></mspace>\n <msub>\n <mi>D</mi>\n <mrow>\n <mn>50</mn>\n <mo>%</mo>\n </mrow>\n </msub>\n </mrow>\n <annotation>$Bladder\\ {{D}_{50{\\mathrm{\\% }}}}$</annotation>\n </semantics></math> (Gy), <span></span><math>\n <semantics>\n <mrow>\n <mi>D</mi>\n <mi>o</mi>\n <mi>s</mi>\n <mi>e</mi>\n <mi>T</mi>\n <mi>y</mi>\n <mi>p</mi>\n <mi>e</mi>\n </mrow>\n <annotation>$DoseType$</annotation>\n </semantics></math>, and <span></span><math>\n <semantics>\n <mrow>\n <mi>S</mi>\n <mi>I</mi>\n <mi>B</mi>\n <mi>D</mi>\n <mi>o</mi>\n <mi>s</mi>\n <mi>e</mi>\n </mrow>\n <annotation>$SIBDose$</annotation>\n </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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.</p>\n </section>\n </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14596","citationCount":"0","resultStr":"{\"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\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (Initial<sub>SOC</sub>) and a reduced margin initial plan (Initial<sub>ART</sub>) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily<sub>SOC</sub> and Daily<sub>ART</sub>) 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 (<span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>D</mi>\\n <mi>a</mi>\\n <mi>i</mi>\\n <mi>l</mi>\\n <mi>y</mi>\\n </mrow>\\n <annotation>${{\\\\Delta}}Daily$</annotation>\\n </semantics></math>= Daily<sub>SOC</sub>-Daily<sub>ART</sub>) versus initial plan differences (<span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <mi>l</mi>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initial$</annotation>\\n </semantics></math>= Initial<sub>SOC</sub>–Initial<sub>ART</sub>) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences (<span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <mi>l</mi>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initial$</annotation>\\n </semantics></math>) of <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>B</mi>\\n <mi>o</mi>\\n <mi>w</mi>\\n <mi>e</mi>\\n <mi>l</mi>\\n <mspace></mspace>\\n <msub>\\n <mi>V</mi>\\n <mrow>\\n <mn>40</mn>\\n <mi>G</mi>\\n <mi>y</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$Bowel\\\\ {{V}_{40Gy}}$</annotation>\\n </semantics></math> (cc), <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>B</mi>\\n <mi>l</mi>\\n <mi>a</mi>\\n <mi>d</mi>\\n <mi>d</mi>\\n <mi>e</mi>\\n <mi>r</mi>\\n <mspace></mspace>\\n <msub>\\n <mi>D</mi>\\n <mrow>\\n <mn>50</mn>\\n <mo>%</mo>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$Bladder\\\\ {{D}_{50{\\\\mathrm{\\\\% }}}}$</annotation>\\n </semantics></math> (Gy), and <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>R</mi>\\n <mi>e</mi>\\n <mi>c</mi>\\n <mi>t</mi>\\n <mi>u</mi>\\n <mi>m</mi>\\n <mspace></mspace>\\n <msub>\\n <mi>D</mi>\\n <mrow>\\n <mn>50</mn>\\n <mo>%</mo>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$Rectum\\\\ {{D}_{50{\\\\mathrm{\\\\% }}}}$</annotation>\\n </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 (<span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <msub>\\n <mi>l</mi>\\n <mrow>\\n <mi>R</mi>\\n <mi>P</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initia{{l}_{RP}}$</annotation>\\n </semantics></math>) and repeated the analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>In both <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <msub>\\n <mi>l</mi>\\n <mrow>\\n <mi>O</mi>\\n <mi>r</mi>\\n <mi>i</mi>\\n <mi>g</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initia{{l}_{Orig}}$</annotation>\\n </semantics></math> and <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <msub>\\n <mi>l</mi>\\n <mrow>\\n <mi>R</mi>\\n <mi>P</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initia{{l}_{RP}}$</annotation>\\n </semantics></math> our multivariate analysis showed low <i>R</i><sup>2</sup> 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., <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <mi>I</mi>\\n <mi>n</mi>\\n <mi>i</mi>\\n <mi>t</mi>\\n <mi>i</mi>\\n <mi>a</mi>\\n <mi>l</mi>\\n </mrow>\\n <annotation>${{\\\\Delta}}Initial$</annotation>\\n </semantics></math> Bowel (V40 Gy), <i>p</i> < 1e−05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>B</mi>\\n <mi>o</mi>\\n <mi>w</mi>\\n <mi>e</mi>\\n <mi>l</mi>\\n <mspace></mspace>\\n <msub>\\n <mi>V</mi>\\n <mrow>\\n <mn>40</mn>\\n <mi>G</mi>\\n <mi>y</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$Bowel\\\\ {{V}_{40Gy}}$</annotation>\\n </semantics></math> (cc), <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>B</mi>\\n <mi>l</mi>\\n <mi>a</mi>\\n <mi>d</mi>\\n <mi>d</mi>\\n <mi>e</mi>\\n <mi>r</mi>\\n <mspace></mspace>\\n <msub>\\n <mi>D</mi>\\n <mrow>\\n <mn>50</mn>\\n <mo>%</mo>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$Bladder\\\\ {{D}_{50{\\\\mathrm{\\\\% }}}}$</annotation>\\n </semantics></math> (Gy), <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>D</mi>\\n <mi>o</mi>\\n <mi>s</mi>\\n <mi>e</mi>\\n <mi>T</mi>\\n <mi>y</mi>\\n <mi>p</mi>\\n <mi>e</mi>\\n </mrow>\\n <annotation>$DoseType$</annotation>\\n </semantics></math>, and <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>S</mi>\\n <mi>I</mi>\\n <mi>B</mi>\\n <mi>D</mi>\\n <mi>o</mi>\\n <mi>s</mi>\\n <mi>e</mi>\\n </mrow>\\n <annotation>$SIBDose$</annotation>\\n </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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.</p>\\n </section>\\n </div>\",\"PeriodicalId\":14989,\"journal\":{\"name\":\"Journal of Applied Clinical Medical Physics\",\"volume\":\"26 3\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14596\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Clinical Medical Physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Clinical Medical Physics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/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

摘要

目的：每日在线适应性放疗（ART）改善了妇科癌症患者的剂量指标，但治疗过程是资源密集型的，需要更长的预约时间和整个适应性团队的额外时间。为了优化资源配置，我们提出了一个识别高优先级患者的模型。方法：对49例回顾性宫颈癌和子宫内膜癌患者，我们计算了两种初始计划：治疗标准护理（InitialSOC）和减少边际初始计划（InitialART），以适应Ethos治疗计划系统。根据治疗CBCT重新分割解剖并在合成CT上计算剂量，确定标准边缘和缩小边缘（DailySOC和DailyART）对应的日剂量。这些初始剂量和每日剂量用于通过多元线性回归估计ART获益（Δ Dai i y$ {{\Delta}} daily $ = DailySOC-DailyART）与初始计划差异（Δ i i i itial$ {{\Delta}} initial $ = InitialSOC-InitialART）。剂量测定的好处与最初计划建模不同(Δ我n t l ${{\三角洲}}初始美元)的B o w e l 40 G y V $肠\ {{V} _ {40 gy}} $ (cc), B l d d e r d 50%美元膀胱\ {{d} _ {50 {\ mathrm {\% }}}}$ ( Gy)和R e c t u m D 50%美元直肠\ {{D} _ {50 {\ mathrm {\% }}}}$ ( Gy)。解剖（完整子宫或子宫切除术后），剂量类型（同时综合增强[SIB] vs单剂量），和/或处方价值。为了建立一个逻辑模型，我们将每个指标中排名前10%的患者分类为高效益患者。然后，我们建立了一个逻辑模型，根据之前的预测因子来预测这些患者。采用留一验证和ROC分析评价准确性。为了提高该预测过程的临床效率，我们还对ΔInitial方案（ΔI I I I I I I I I al P ${{\Delta}}Initia{{l}_{RP}}$）创建了基于知识的方案，并重复分析。结果:在两个Δ我n t l O r I g ${{\三角洲}}Initia {{l} _{源自}}$和Δ我n t l r P ${{\三角洲}}Initia {{l} _ {RP}} $我们的多元分析显示低R2值0.34 - -0.52和0.14 - -0.38。最重要的在每个多变量预测模型相应的∆初始指标(例如,Δ我n t l ${{\三角洲}}初始美元肠(V40 Gy), p V B o w e l 40 G y美元肠\ {{V} _ {40 Gy}} $ (cc), B l d d e r d 50%美元膀胱\ {{d} _ {50 {\ mathrm {\% }}}}$ ( Gy), D o s e T y p e DoseType,美元和s I B D o s e SIBDose美元处方。原始计划模型和知识计划模型的AUC分别为0.85和0.78。敏感性和特异性分别为0.92/0.72和0.69/0.80。结论：这种方法将允许诊所优先考虑患者进行资源密集型的日常在线ART。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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

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.

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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