A Radiation Oncology Based Electronic Health Record in an Integrated Radiation Oncology Network

Purpose: The goal of this ongoing project is to develop and integrate a comprehensive electronic health record (EHR) throughout a multi-facility radiation oncology network to facilitate more efficient workflow and improve overall patient care and safety. Methodology: We required that the EHR provide pre-defined record and verify capability for radiation treatment while still providing a robust clinical health record. In 1996, we began to integrate the Local Area Network Treatment Information System (LANTIS®) across the West Penn Allegheny Radiation Oncology Network (currently including 9 sites). By 2001, we began modifying and expanding the assessment components and creating user-defined templates and have developed a comprehensive electronic health record across our network. Results: In addition to access to the technical record and verify information and imaging obtained for image-guided therapy, we designed and customized 6 modules according to our networks needs to facilitate information acquisition, tracking, and analysis as follows: 1) Demographics/scheduling; 2) Charge codes; 3) Transcription/clinical documents; 4) Clinical/technical assessments; 5) Physician orders 6) Quality assurance pathways. Each module was developed to acquire specific technical/clinical data prospectively in an efficient manner by various staff within the department in a format that facilitates data queries for outcomes/statistical analyses and promotes standardized quality guidelines resulting in a more efficient workflow and improved patient safety and care. Conclusions: Development of a comprehensive EHR across a radiation oncology network is feasible and can be customized to promote clinical/technical standards, facilitate outcomes studies, and improve communication and peer review. The EHR has improved patient care and network integration across a multi-facility radiation oncology system and has markedly reduced the flow and storage of paper across the network

A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes: A genetic algorithm for radiotherapy outcome modeling

A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies

A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes

A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies

Long-term primary results of accelerated partial breast irradiation after breast-conserving surgery for early-stage breast cancer: a randomised, phase 3, equivalence trial

BackgroundWhole-breast irradiation after breast-conserving surgery for patients with early-stage breast cancer decreases ipsilateral breast-tumour recurrence (IBTR), yielding comparable results to mastectomy. It is unknown whether accelerated partial breast irradiation (APBI) to only the tumour-bearing quadrant, which shortens treatment duration, is equally effective. In our trial, we investigated whether APBI provides equivalent local tumour control after lumpectomy compared with whole-breast irradiation.MethodsWe did this randomised, phase 3, equivalence trial (NSABP B-39/RTOG 0413) in 154 clinical centres in the USA, Canada, Ireland, and Israel. Adult women (>18 years) with early-stage (0, I, or II; no evidence of distant metastases, but up to three axillary nodes could be positive) breast cancer (tumour size ≤3 cm; including all histologies and multifocal breast cancers), who had had lumpectomy with negative (ie, no detectable cancer cells) surgical margins, were randomly assigned (1:1) using a biased-coin-based minimisation algorithm to receive either whole-breast irradiation (whole-breast irradiation group) or APBI (APBI group). Whole-breast irradiation was delivered in 25 daily fractions of 50 Gy over 5 weeks, with or without a supplemental boost to the tumour bed, and APBI was delivered as 34 Gy of brachytherapy or 38·5 Gy of external bream radiation therapy in 10 fractions, over 5 treatment days within an 8-day period. Randomisation was stratified by disease stage, menopausal status, hormone-receptor status, and intention to receive chemotherapy. Patients, investigators, and statisticians could not be masked to treatment allocation. The primary outcome of invasive and non-invasive IBTR as a first recurrence was analysed in the intention-to-treat population, excluding those patients who were lost to follow-up, with an equivalency test on the basis of a 50% margin increase in the hazard ratio (90% CI for the observed HR between 0·667 and 1·5 for equivalence) and a Cox proportional hazard model. Survival was assessed by intention to treat, and sensitivity analyses were done in the per-protocol population. This trial is registered with ClinicalTrials.gov, NCT00103181.FindingsBetween March 21, 2005, and April 16, 2013, 4216 women were enrolled. 2109 were assigned to the whole-breast irradiation group and 2107 were assigned to the APBI group. 70 patients from the whole-breast irradiation group and 14 from the APBI group withdrew consent or were lost to follow-up at this stage, so 2039 and 2093 patients respectively were available for survival analysis. Further, three and four patients respectively were lost to clinical follow-up (ie, survival status was assessed by phone but no physical examination was done), leaving 2036 patients in the whole-breast irradiation group and 2089 in the APBI group evaluable for the primary outcome. At a median follow-up of 10·2 years (IQR 7·5-11·5), 90 (4%) of 2089 women eligible for the primary outcome in the APBI group and 71 (3%) of 2036 women in the whole-breast irradiation group had an IBTR (HR 1·22, 90% CI 0·94-1·58). The 10-year cumulative incidence of IBTR was 4·6% (95% CI 3·7-5·7) in the APBI group versus 3·9% (3·1-5·0) in the whole-breast irradiation group. 44 (2%) of 2039 patients in the whole-breast irradiation group and 49 (2%) of 2093 patients in the APBI group died from recurring breast cancer. There were no treatment-related deaths. Second cancers and treatment-related toxicities were similar between the two groups. 2020 patients in the whole-breast irradiation group and 2089 in APBI group had available data on adverse events. The highest toxicity grade reported was: grade 1 in 845 (40%), grade 2 in 921 (44%), and grade 3 in 201 (10%) patients in the APBI group, compared with grade 1 in 626 (31%), grade 2 in 1193 (59%), and grade 3 in 143 (7%) in the whole-breast irradiation group.InterpretationAPBI did not meet the criteria for equivalence to whole-breast irradiation in controlling IBTR for breast-conserving therapy. Our trial had broad eligibility criteria, leading to a large, heterogeneous pool of patients and sufficient power to detect treatment equivalence, but was not designed to test equivalence in patient subgroups or outcomes from different APBI techniques. For patients with early-stage breast cancer, our findings support whole-breast irradiation following lumpectomy; however, with an absolute difference of less than 1% in the 10-year cumulative incidence of IBTR, APBI might be an acceptable alternative for some women.FundingNational Cancer Institute, US Department of Health and Human Services