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Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan™.

ABSTRACT: INTRODUCTION:To create and clinically validate knowledge-based planning (KBP) models for gynaecologic (GYN) and rectal cancer patients. Assessment of ecologic generalisability and predictive validity of conventional planning versus single calculation KBP was reviewed against practical metrics of planning time (PT) and radiation oncologist plan preference. METHOD:Study cohorts were 34 and 42 consecutively treated GYN and rectal cancer patients dosimetrically archived within the centre's research databank. For model training, structures and dose distributions from 22 and 32 GYN and rectal volumetric-modulated arc therapy (VMAT) plans were used in RapidPlan™. Prescription doses ranged from 45 to 60Gy in 25 fractions using a simultaneous integrated boost to 2-4 targets and up to 9 organ-at-risk volumes. For model validation, 12 GYN and 10 rectal were independent of the archive and a single pass KBP VMAT plan was created. Each plan was evaluated against the archived treated plan under blinded conditions for radiation oncologist preference using standard dosimetric quality parameters. RESULTS:All 22 plans generated in the KBP validation cohort met pre-set GYN and rectal cancer dosimetric quality metrics. Fifty per cent of GYN plans and eighty per cent of rectal plans were judged superior to the manually optimised plans. KBP reduced PT considerably for both tumour sites. CONCLUSION:Single pass KBP for GYN and rectal cancer patients produced clinically acceptable treatment plans which were non-inferior to conventionally optimised plans in 14 of 22 cases. Efficiencies captured by KBP will have predictable impacts on institutional workflows and resource allocation to facilitate adaptive planning.

SUBMITTER: Shepherd M

PROVIDER: S-EPMC7476182 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan<sup>™</sup>.

Shepherd Meegan M Bromley Regina R Stevens Mark M Morgia Marita M Kneebone Andrew A Hruby George G Atyeo John J Eade Thomas T

Journal of medical radiation sciences 20200525 3

<h4>Introduction</h4>To create and clinically validate knowledge-based planning (KBP) models for gynaecologic (GYN) and rectal cancer patients. Assessment of ecologic generalisability and predictive validity of conventional planning versus single calculation KBP was reviewed against practical metrics of planning time (PT) and radiation oncologist plan preference.<h4>Method</h4>Study cohorts were 34 and 42 consecutively treated GYN and rectal cancer patients dosimetrically archived within the cen ...[more]

PMID: 32450610

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Project description:IntroductionGynaecological cancers collectively account for almost 10% of cancer diagnoses made in Australian women. The extent of variation in gynaecological cancer survival rates and treatment outcomes across Australia is not well documented. The purpose of the clinical quality registry described in this paper is to systematically monitor and improve quality of care provided to these women, and facilitate clinical process improvements to ensure better patient outcomes and greater adherence to best practice care. The registry infrastructure has been developed in conjunction alongside the inaugural ovarian, tubal and peritoneal (OTP) module, allowing for concurrent piloting of the methodology and one module. Additional tumour modules will be developed in time to cover the other gynaecological tumour types.Method and analysisThe National Gynae-Oncology Registry (NGOR) aims to capture clinical data on all newly diagnosed cancers of the uterus, ovary, fallopian tubes, peritoneum, cervix, vulva and vagina in Australia with a view to using these data to support improved clinical care and increased adherence to 'best practice'. Data are sourced from existing clinical databases maintained by clinicians and/or hospital gynaecological cancer units. A pilot phase incorporating only OTP cancers has recently been conducted to assess the feasibility of the registry methodology and assess the support of a quality initiative of this nature among clinicians and other key stakeholders.Ethics and disseminationThe NGOR has received National Mutual Acceptance (NMA) ethics approval from Monash Health Human Research Ethics Committee (HREC), NMA HREC Reference Number: HREC/17/MonH/198. We also have approval from Mercy Health HREC and University of Tasmania HREC. Data will be routinely reported back to participating sites illustrating their performance against measures of agreed best practice. It is through this feedback system that the registry will support changes to quality of care and improved patient outcomes.

Project description:The clinical utility of next-generation sequencing (NGS) for a diverse range of targets is expanding, increasing the need for multiplexed analysis of both DNA and RNA. However, translation into daily use requires a rigorous and comprehensive validation strategy. The aim of this clinical validation was to assess the performance of the Ion Torrent Personal Genome Machine (IonPGM™) and validate the Oncomine™ Focus DNA and RNA Fusion panels for clinical application in solid tumour testing of formalin-fixed, paraffin-embedded (FFPE) tissue. Using a mixture of routine FFPE and reference material across a variety of tissue and specimen types, we sequenced 86 and 31 samples on the Oncomine™ Focus DNA and RNA Fusion assays, respectively. This validation considered a number of parameters including the clinical robustness of the bioinformatics pipeline for variant detection and interpretation. The Oncomine™ Focus DNA assay had a sample and variant-based sensitivity of 99.1 and 97.1%, respectively, and an assay specificity of 100%. The Oncomine™ Focus Fusion panel had a good sensitivity and specificity based upon the samples assessed, however requires further validation to confirm findings due to limited sample numbers. We observed a good sequencing performance based upon amplicon, gene (hotspot variants within gene) and sample specific analysis with 92% of clinical samples obtaining an average amplicon coverage above 500X. Detection of some indels was challenging for the routine IonReporter™ workflow; however, the addition of NextGENe® software improved indel identification demonstrating the importance of both bench and bioinformatic validation. With an increasing number of clinically actionable targets requiring a variety of methodologies, NGS provides a cost-effective and time-saving methodology to assess multiple targets across different modalities. We suggest the use of multiple analysis software to ensure identification of clinically applicable variants.

Project description:ObjectiveThe Diabetes Early Re-admission Risk Indicator (DERRI™) was previously developed and internally validated as a tool to predict the risk of all-cause re-admission within 30 days of discharge (30-day re-admission) of hospitalized patients with diabetes. In this study, the predictive performance of the DERRI™ with and without additional predictors was assessed in an external sample.MethodsWe conducted a retrospective cohort study of adult patients with diabetes discharged from two academic medical centers between January 1, 2000 and December 31, 2014. We applied the previously developed DERRI™, which includes admission laboratory results, sociodemographics, a diagnosis of certain comorbidities, and recent discharge information, and evaluated the effect of adding metabolic indicators on predictive performance using multivariable logistic regression. Total cholesterol and hemoglobin A1c (A1c) were selected based on clinical relevance and univariate association with 30-day re-admission.ResultsAmong 105,974 discharges, 19,032 (18.0%) were followed by 30-day re-admission for any cause. The DERRI™ had a C-statistic of 0.634 for 30-day re-admission. Total cholesterol was the lipid parameter most strongly associated with 30-day re-admission. The DERRI™ predictors A1c and total cholesterol were significantly associated with 30-day re-admission; however, their addition to the DERRI™ did not significantly change model performance (C-statistic, 0.643 [95% confidence interval, 0.638 to 0.647]; P = .92).ConclusionPerformance of the DERRI™ in this external cohort was modest but comparable to other re-admission prediction models. Addition of A1c and total cholesterol to the DERRI™ did not significantly improve performance. Although the DERRI™ may be useful to direct resources toward diabetes patients at higher risk, better prediction is needed.AbbreviationsA1c = hemoglobin A1c; CI = confidence interval; DERRI™ = Diabetes Early Re-admission Risk Indicator; GEE = generalized estimating equation; HDL-C = high-density-lipoprotein cholesterol; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; LDL-C = low-density-lipoprotein cholesterol.

Project description:PurposeTo demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system.Methods and materialsWe analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V10 (percentage volume of PBM receiving at least 10 Gy dose) and V20 (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V40 (absolute volume of bowel receiving at least 40 Gy dose) and V45 (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning "routine"). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBPFINAL), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBPFINAL plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets.ResultsKBPFINAL plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBPFINAL versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBPFINAL plans, indicating lowered probability of hematologic toxicity.ConclusionsThis work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.

Dataset Information

Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan™.

Publications

Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan<sup>™</sup>.

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