Complexity Score Research Articles

Overcoming Problems Caused by Offset Distance of Multiple Targets in Single-isocenter Volumetric Modulated Arc Therapy Planning for Stereotactic Radiosurgery.

The purpose of the study is to investigate the impact of large target offset distances on the dose distribution and gamma passing rate (GPR) in single-isocenter multiple-target stereotactic radiosurgery (SIMT SRS) using volumetric modulated arc therapy (VMAT) with a flattening filter-free (FFF) beam from a linear accelerator. Two targets with a diameter of 1 cm were offset by "±2, ±4, and ±6 cm from the isocenter in a verification phantom for head SRS (20 Gy/fr). The VMAT plans were created using collimator angles that ensured the two targets did not share a leaf pair from the multi-leaf collimator. To evaluate the low-dose spread intermediate dose spill (R50%), GPRs were measured with a criterion of 3%/2 mm using an electronic portal imaging device and evaluated using monitor unit (MU), modulation complexity score for VMAT (MCSv), and leaf travel (LT) parameters. For offsets of 2, 4, and 6 cm, the respective parameters were: R50%, 4.75 ± 0.36, 5.13 ± 0.36, and 5.11 ± 0.33; GPR, 95.01%, 93.82%, and 90.67%; MU, 5893 ± 186, 5825 ± 286, and 5810 ± 396; MCSv, 0.24, 0.16, and 0.13; and LT, 189.21 ± 36.04, 327.69 ± 67.01, and 430.39 ± 114.34 mm. There was a spread in the low-dose region from offsets of ≥4 cm and the GPR negatively correlated with LT (r = -0.762). There was minimal correlation between GPR and MU or MCSv. In SIMT SRS VMAT plans with an FFF beam from a linear accelerator, target offsets of <4 cm from the isocenter can minimize the volume of the low-dose region receiving 10 Gy or more. During treatment planning, it is important to choose gantry, couch, and collimator angles that minimize LT and thereby improve the GPR.

Efficacy of Seoul National University Renal Stone Complexity Score for Predicting Stone Free Rate after Percutaneous Nephrolithotomy

Aim: To determine the frequency of stone clearance following single tract percutaneous nephrolithotomy and evaluate the reliability of the renal stone. Study Design: Descriptive study Place and duration of study: Department of Urology, SIUT, Karachi from 1st January 2020 to 31st October 2021. Methodology: One hundred and forty four patients were enrolled. Patients were classified into three groups, low, medium and high score group based on Seoul Renal Stone Complexity Scoring System. To determine the SFR, all patients were followed with ultrasound and X-ray KUB for 30th day after the surgery. Results: The patients' mean age was 34.51±12.57 years. The mean SReSC score was 4.00±1.96. There were 106(73.61%) males and 38(26.39%) patients were females. SReSC score was low in 71 patients (49.31%), medium in 45 patients (31.25%), and high in 28 patients (19.44%). 114(79.17%) patients were found to be stone-free. The SFR was greater among patients with a low S-ReSC score 98.6%. Conclusion: The S-ReCSC scoring system is valuable in predicting of the post PCNL stone free rate. Keywords: Seoul National University Renal Stone Complexity Score, Stone free rate, Percutaneous Nephrolithotomy.

Music Festivals in Modern Italy During the Covid-19 Pandemic

"This study analyses one of the complex problems of modern artistic discourse – the functioning of a music festival as a special form of intercultural dialogue and its integration into a modern cultural medium. The article reveals the issues of the relationship of its components, such as the conditions for the implementation of intercultural communication in the context of a music festival; the morphological space of the multi-layer score of this artistic phenomenon; forms of intercultural communication, and sociocultural constraints associated with the Covid-19 pandemic. The purpose of this study was to analyze the music festival as one of the forms of polyculture dialogue and its functioning in the new realities of the Covid-19 pandemic. In this regard, both theoretical (semantic analysis) and practical (comparative analysis) research methods were used. As a result of the study, it was proven that a music festival as one of the forms of intercultural dialogue allows realizing different semantic levels of culture, enriches spirituality, and contributes to the consolidation of representatives of different cultures, leading to a constructive solution of the problems of artistic discourse; it was revealed that during the Covid-19 pandemic, not all genre and stylistic forms could be realized, due to the difficult situation of socio-cultural isolation; a classification of new formats for holding music festivals was proposed, which take into account the current conditions for its functioning in the context of the Covid-19 pandemic. The practical significance of this study is that its results can be used for further analysis of the mechanisms of functioning and broadcasting of a polyphonically complex score of a music festival. Keywords: music festival, the musical life of modern Italy, artistic dialogue, interaction, multi-layered score of a creative project, multicultural space "

Association between CT-based body composition assessment and patient outcomes during neoadjuvant chemotherapy for epithelial ovarian cancer

ObjectiveThe aim of this study was to characterize the body composition of patients undergoing neoadjuvant chemotherapy (NACT) for epithelial ovarian cancer (EOC), identify factors associated with sarcopenia at diagnosis, and evaluate the impact of pretreatment sarcopenia and changes in body composition parameters during therapy on perioperative and disease-related outcomes. MethodsPatients undergoing NACT for EOC between 2008 and 2020 were identified. Pre-treatment and post-treatment contrast-enhanced CT scans were reviewed to determine skeletal muscle index (SMI) and visceral adipose tissue (VAT) area at the mid-fourth lumbar vertebral level. SMI and VAT were analyzed for association with clinical and treatment variables. Results174 patients were identified. Mean pretreatment SMI and VAT were 38.3 cm2/m2 ± 7.9 and 51.2 cm2/m2 ± 34.3, respectively. Comparatively, mean post-treatment SMI and VAT were 37.8 cm2/m2 ± 7.9 and 43.7 cm2/m2 ± 29.7, respectively. Most patients exhibited an overall decrease in SMI from pretreatment to posttreatment scans. Caucasian race, older age, and lower body mass index at diagnosis were associated with lower pretreatment SMI. Lower pre-treatment SMI was associated with lower surgical complexity scores (p < 0.001) and estimated blood loss (p = 0.029). Decrease in SMI after NACT was associated with increased rates of ICU admissions and length of stay. While there was no association between SMI and overall survival (OS) or progression-free survival (PFS), >2% decrease per 100 days in VAT was significantly associated with worse OS. ConclusionsPatients with lower pretreatment SMI tend to undergo less complex surgery than those with higher SMI despite NACT. Decrease in VAT may be a potential indicator of worse OS. Information on body composition can aid in clinical decision making in patients with EOC.

Sentence-level complexity in Russian: An evaluation of BERT and graph neural networks.

Sentence-level complexity evaluation (SCE) can be formulated as assigning a given sentence a complexity score: either as a category, or a single value. SCE task can be treated as an intermediate step for text complexity prediction, text simplification, lexical complexity prediction, etc. What is more, robust prediction of a single sentence complexity needs much shorter text fragments than the ones typically required to robustly evaluate text complexity. Morphosyntactic and lexical features have proved their vital role as predictors in the state-of-the-art deep neural models for sentence categorization. However, a common issue is the interpretability of deep neural network results. This paper presents testing and comparing several approaches to predict both absolute and relative sentence complexity in Russian. The evaluation involves Russian BERT, Transformer, SVM with features from sentence embeddings, and a graph neural network. Such a comparison is done for the first time for the Russian language. Pre-trained language models outperform graph neural networks, that incorporate the syntactical dependency tree of a sentence. The graph neural networks perform better than Transformer and SVM classifiers that employ sentence embeddings. Predictions of the proposed graph neural network architecture can be easily explained.

Algorithmic complexity stratification for congenital heart disease patients

BackgroundCongenital Heart Disease (CHD) encompasses a huge variety of rare diagnoses that range in complexity and comorbidity. To help build clinical guidelines, plan health services and conduct statistically powerful research on such a disparate set of diseases there have been various attempts to group pathologies into mild, moderate, or severe disease. So far, however, these complexity scores have required manual specialist input for every case, and are therefore missing in large databases where this is impractical, or quickly outdated when guidelines are revised. MethodsWe used the up-to-date European Society of Cardiology guidelines to create an algorithm to assign complexity scores to CHD patients using only their diagnosis list. Two CHD specialists then independently assigned complexity scores to a random sample of patients. ResultsOur algorithm was 96% accurate where both specialists agreed on a complexity score; this occurred 68% of the time overall, and 79% of the time in moderate or complex CHD. The algorithm “failed” mainly when diagnoses were insufficiently specific, usually for septal defects (where size was unspecified), or where complexity depends on the procedure performed (e.g. atrial/arterial switch for transposition of the great arteries). ConclusionsWe were able to algorithmically determine the complexity scores of a majority of patients with CHD based on their diagnosis list alone. This could allow for automatic complexity scoring of most patients in large CHD databases, for example our own Registry of the Congenital Heart Alliance of Australia and New Zealand. This will facilitate targeted research into the management, outcomes and burden of CHD.

Scene Complexity Captures the Detail Trace of Visual Episodic Memory

Memory research has established that human observers use schemas, mental constructs that represent scene elements and relationships between those elements, as the basis for encoding information into visual episodic memory. Newly developed computational methods now allow the extraction of this schema information from the human observer via two-dimensional maps known as Visual Memory Schema (VMS) maps (Kyle-Davidson, Bors & Evans, 2019). VMS maps reveal which regions in a scene image are responsible for a human recalling that scene. VMS Maps are highly consistent among observers and are not explained solely by computational saliency models or eye-fixations. However, previous work also suggests that humans, in addition to schemas, retain rich detail of the scene, and the presence of that detailed information aids in later recognition of the same. Findings show that scenes considered to be more detailed (scenes with man-made elements) are remembered better than less detailed scenes (natural scene). Similarly, images that have detail removed (compared to a source image) appear less memorable than images that maintain the original detail (Baddeley & Evans, 2019). We investigated whether perceptual image complexity could stand as an analogue for the detail trace in visual memory, exploring how the complexity of an image relates to that image's memorability. Our approach was to conduct a behavioural experiment gathering two-dimensional complexity information from humans for a scene dataset that already has corresponding schema information (VMS Maps), which allowed us to explore complexity and memorability from a two-dimensional perspective. Next, we trained a neural network to predict complexity scores, and investigate this relationship for another scene memory dataset, now comparing the predicted complexity scores with ground truth memory for the images. The findings indicate that both extracted, and predicted perceptual complexity of the scene, relates to how well observers remember that same scene.

Integrative Complexity, COVID-19, and Political Ideology

This study explores the relationship between political ideology and integrative complexity in terms of COVID-19 news coverage. Briefly, integrative complexity is a psycholinguistic construct that examines cognitive structure. Based on a sample of COVID-19 news articles sourced from liberal, moderate, and conservative news networks, the results show liberal and moderate networks displayed higher complexity scores than conservative networks. The results further confirm a complexity pattern seen in previous research — liberals and moderates tend to demonstrate more complex rhetoric than conservatives. Moreover, the conservative networks’ lower scores may be explained by the predominance of misinformation in their coverage of COVID-19.

Racial and Ethnic Differences in Occupational Complexity and Late‐Life Cognition

AbstractBackgroundStructural racism affects employment opportunities, resulting in occupational segregation and potential racial and ethnic health disparities. Research examining the association between occupational complexity and late‐life cognition within and across racial and ethnic groups is limited. We aim to address this research gap using the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) cohort of Asian, Black, Latino and White community‐dwelling participants aged 65+ years.MethodsWe linked occupational complexity scores (from the Dictionary of Occupational Titles) for data (range: 0‐6), people (0‐8), and things (0‐7) to self‐reported main lifetime occupation. We assessed baseline domain‐specific cognition (executive functioning, verbal episodic memory, and semantic memory) using the Spanish and English Neuropsychological Assessment Scales. Using linear regression models, we estimated associations between each occupational complexity measure and domain‐specific z‐scores, adjusting for age, sex, race and ethnicity, and education. We also assessed effect modification by race and ethnicity.ResultsAmong 1,548 participants (mean age = 76 years), 24% identified as Asian, 27% as Black, 19% as Latino, 29% as White, and 50% had a college degree or higher (Table 1). Occupational complexity with data and people varied across race and ethnicity (p‐value: &gt;0.001 and 0.016, respectively), but not for complexity with things. Greater occupational complexity with data was associated with higher executive function (β = 0.05 SD units; 95% CI:0.02‐0.09), semantic memory (0.07 SD units; 95% CI:0.04‐0.11) and verbal episodic memory (0.06 SD units; 95% CI:0.02‐0.10) (Figure 1). Complexity with people was associated with higher executive function and semantic memory only. Complexity with things was not associated with any cognitive domain. Effect modification by race and ethnicity was significant, such that the association between greater occupational complexity with data and higher semantic memory was stronger among Latinos, compared to other racial groups.ConclusionWithin a diverse cohort, greater occupational complexity with data and people were associated with higher late‐life cognition. Occupational complexity scores varied by race and ethnicity, as well as its associations with cognitive domains. These findings suggest that racial and ethnic differences in occupational complexity may contribute to cognitive health disparities.

Association of Residual Ischemic Disease With Clinical Outcomes After Percutaneous Coronary Intervention

BackgroundAnatomical scoring systems have been used to assess completeness of revascularization but are challenging to apply to large real-world datasets. ObjectivesThe aim of this study was to assess the prevalence of complete revascularization and its association with longitudinal clinical outcomes in the U.S. Department of Veterans Affairs (VA) health care system using an automatically computed anatomic complexity score. MethodsPatients undergoing percutaneous coronary intervention (PCI) between October 1, 2007, and September 30, 2020, were identified, and the burden of prerevascularization and postrevascularization ischemic disease was quantified using the VA SYNTAX (Synergy Between PCI With Taxus and Cardiac Surgery) score. The association between residual VA SYNTAX score and long-term major adverse cardiovascular events (MACE; death, myocardial infarction, repeat revascularization, and stroke) was assessed. ResultsA total of 57,476 veterans underwent PCI during the study period. After adjustment, the highest tertile of residual VA SYNTAX score was associated with increased hazard of MACE (HR: 2.06; 95% CI: 1.98-2.15) and death (HR: 1.50; 95% CI: 1.41-1.59) at 3 years compared to complete revascularization (residual VA SYNTAX score = 0). Hazard of 1- and 3-year MACE increased as a function of residual disease, regardless of baseline disease severity or initial presentation with acute or chronic coronary syndrome. ConclusionsResidual ischemic disease was strongly associated with long-term clinical outcomes in a contemporary national cohort of PCI patients. Automatically computed anatomic complexity scores can be used to assess the longitudinal risk for residual ischemic disease after PCI and may be implemented to improve interventional quality.

Measuring clinical trial set-up complexity: development and content validation of a pharmacy scoring tool to support workload planning

Abstract Introduction Clinical trials are essential for advancing medical knowledge and patient care. Pharmacy plays a vital role in research delivery, safeguarding participants, healthcare professionals and Trusts by ensuring Investigational Medicinal Products (IMPs) are procured, handled, stored, dispensed and used safely. Data have emerged indicating that clinical trial protocols are increasingly more complex for site personnel to execute. Whilst various tools have been developed to support workload planning and costing of clinical trials for clinicians and nursing personnel, the equivalent tool for pharmacy activities is not available.1,2 Aim This study aims to locally develop and validate a pharmacy scoring tool to assess clinical trial complexity at the set-up stage. Objectives are to develop pharmacy-scoring tool to quantify clinical trials execution complexity with respect to pharmacy activities and determine Content Validity Index (CVI) and Inter-Rater Agreement (IRA) of the scoring tool through an internal vetting process. Methods A three-stage process was used to develop and validate the scoring tool. Two researchers contextualised the pharmacy–specific items, organised the content and constructed the tool based on suitable elements for inclusion through a targeted literature search and outputs from an expert panel meeting. Four experts independently rated each items on its relevance, clarity and alignment with clinical trial complexity considerations at the set-up stage using a 4-point Likert scale. Following data analysis, researchers made a judgement on whether to retain, modify, omit or add new items to the tool. Where the desired level of content validity had not been achieved for an item, revisions were made following a group discussion and the tool was re-distributed to the same expert panel for a second round of assessment. Item level-CVI (I-CVI) and tool level-CVI (average-CVI) was calculated for each round. Four experts then utilised the revised tool to retrospectively review 20 random studies to evaluate clinical trial complexity scores independently. IRA among assessors was measured for each individual pharmacy-specific item by dividing the responses into dichotomous ratings (one or two vs. three or four). The study was deemed a service evaluation and approved by the Trust Pharmacy Research and Audit Group therefore ethical submission was waived. Results Fourteen pharmacy-specific items were identified for inclusion in the tool with a possible score of 0-3 points per item. The highest possible complexity score when adding up all items is 42 points with a scale of low 0-14, moderate &amp;gt;14-28, and high &amp;gt;28-42. Following two rounds of content validity assessment where clarity revisions were made to four items, 14 out of 14 scored I-CVI of 1 with overall tool level average-CVI score of 1. The IRA shows 100% agreement by item and tool. Discussion/Conclusion This clinical trial’s complexity-scoring tool is the first of its kind tailored for pharmacy set-up in the UK. Overall, the results from this study support validity and reliability (content and construct) of this locally developed tool. The formal agreement on the use of this tool with the Trust Research Lead has better facilitated workload and capacity planning as well as ensuing costings for Pharmacy clinical trial activities.

Nomogram to predict postoperative complications after cytoreductive surgery for advanced epithelial ovarian cancer: A multicenter retrospective cohort study.

To establish nomograms to predict the risk of postoperative complications following cytoreductive surgery in patients with advanced epithelial ovarian cancer (AEOC). A multicenter retrospective cohort study that included patients with FIGO stage IIIC-IV epithelial ovarian cancer who underwent cytoreductive surgery was designed. By using univariate and multivariate analyses, patient preoperative characteristics were used to predict the risk of postoperative complications. Multivariate modeling was used to develop Nomograms. Overall, 585 AEOC patients were included for analysis (training cohort = 426, extrapolation cohort = 159). According to the findings, the training cohort observed an incidence of postoperative overall and severe complications of 28.87% and 6.10%, respectively. Modified frailty index (mFI) (OR 1.96 and 2.18), FIGO stage (OR 2.31 and 3.22), and Surgical Complexity Score (SCS) (OR 1.16 and 1.23) were the clinical factors that were most substantially associated to the incidence of overall and severe complications, respectively. The resulting nomograms demonstrated great internal discrimination, good consistency, and stable calibration, with C-index of 0.74 and 0.78 for overall and severe complications prediction, respectively. A satisfactory external discrimination was also indicated by the extrapolation cohort, with the C-index for predicting overall and severe complications being 0.92 and 0.91, respectively. The risk of considerable postoperative morbidity exists after cytoreductive surgery for AEOC. These two nomograms with good discrimination and calibration might be useful to guide clinical decision-making and help doctors assess the probability of postoperative complications for AEOC patients.

P027Abortion complexity and abortion identity: Findings from the ohio survey of women

<h3>Objectives</h3> Despite framing of abortion attitudes as polarized, attitudes may be more complex. After classifying women by their abortion identity (ie, pro-life, pro-choice, or neither/both), we examined the complexity of their abortion attitude. <h3>Methods</h3> We used 2018–2019 data from a population-representative sample of Ohio women aged 18–44 to assess views on abortion based on: 1) availability, 2) acceptability, 3) fear of imprisonment, 4) feelings toward providers, and 5) views toward federal bans. We calculated complexity scores for the individual's degree of variation in responses and compared across abortion identity. Respondents' views were categorized as polarized, mixed, or complex. We used ordered logistic regression to evaluate the association between sociodemographic characteristics and complexity scores. <h3>Results</h3> Overall, most (53%) had polarized abortion views. Among pro-life women, 46% were polarized and 28% held complex views. In comparison, among pro-choice women, 78% were polarized and only 2% held complex views. Among those identifying as neither/both, the plurality (44%) had mixed views. Eight factors were associated with having higher complexity scores: rural Appalachia residence, lower socioeconomic status, older age, being married, having live children, not knowing someone who had an abortion, attending religious services more regularly, and holding middle-ground political ideologies. <h3>Conclusions</h3> While most women in Ohio held polarized views on abortion, those with pro-life or neither/both abortion identity had more complex views. These findings suggest that Ohio policies that completely ban abortion are incongruent with public sentiment.

Predicting in-hospital mortality in pulmonary embolism patients: development and external validation of the PATHOS score.

According to the 2019 European Society of Cardiology (ESC) guidelines on pulmonary embolism (PE), prognosis is calculated using the Pulmonary Embolism Severity Index (PESI), a complex score with debated validity, or simplified PESI (sPESI). We have developed and validated a new risk score for in-hospital mortality (IHM) of patients with PE in the emergency department. This retrospective, dual-center cohort study was conducted in the emergency departments of two third-level university hospitals. Patients aged >18 years with a contrast-enhanced computed tomography-confirmed PE were included. Clinical variables and laboratory tests were evaluated blindly to IHM. Multivariable logistic regression was performed to identify the new score's predictors, and the new score was compared with the PESI, sPESI, and shock index. A total of 1,358 patients were included in this study: 586 in the derivation cohort and 772 in the validation cohort, with a global 10.6% of IHM. The PATHOS scores were developed using independent variables to predict mortality: platelet count, age, troponin, heart rate, oxygenation, and systolic blood pressure. The PATHOS score showed good calibration and high discrimination, with an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [CI], 0.77-0.89) in the derivation population and 0.74 (95% CI, 0.68-0.80) in the validation cohort, which is significantly higher than the PESI, sPESI, and shock index in both cohorts (P<0.01 for all comparisons). PATHOS is a simple and effective prognostic score for predicting IHM in patients with PE in an emergency setting.

Comprehensive dosimetric and clinical evaluation of lexicographic optimization-based planning for cervical cancer.

In this study, a not yet commercially available fully-automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), was validated for cervical cancer. Twenty-four mono-institutional consecutive treatment plans (50 Gy/25 fx) delivered between November 2019 and April 2022 were retrospectively selected. The automatic re-planning was performed by mCycle, implemented in the Monaco TPS research version (v5.59.13), in which the LO and Multicriterial Optimization (MCO) are coupled with Monte Carlo calculation. mCycle optimization follows an a priori assigned priority list, the so-called Wish List (WL), representing a dialogue between the radiation oncologist and the planner, setting hard constraints and following objectives. The WL was tuned on a patient subset according to the institution's clinical protocol to obtain an optimal plan in a single optimization. This robust WL was then used to automatically re-plan the remaining patients. Manual plans (MP) and mCycle plans (mCP) were compared in terms of dose distributions, complexity (modulation complexity score, MCS), and delivery accuracy (perpendicular diode matrices, gamma analysis-passing ratio, PR). Their clinical acceptability was assessed through the blind choice of two radiation oncologists. Finally, a global quality score index (SI) was defined to gather into a single number the plan evaluation process. The WL tuning requested four patients. The 20 automated re-planning tasks took three working days. The median optimization and calculation time can be estimated at 4h and just over 1h per MP and mCP, respectively. The dose comparison showed a comparable organ-at-risk spare. The planning target volume coverage increased (V95%: MP 98.0% [95.6-99.3]; mCP 99.2%[89.7-99.9], p >0.05). A significant increase has been registered in MCS (MP 0.29 [0.24-0.34]; mCP 0.26 [0.23-0.30], p <0.05) without affecting delivery accuracy (PR (3%/3mm): MP 97.0% [92.7-99.2]; mCP 97.1% [95.0-98.6], p>0.05). In the blind choice, all mCP results were clinically acceptable and chosen over MP in more than 75% of cases. The median SI score was 0.69 [0.41-0.84] and 0.73 [0.51-0.82] for MP and mCP, respectively (p >0.05). mCycle plans were comparable to clinical manual plans, more complex but accurately deliverable and registering a similar SI. Automated plans outperformed manual plans in blinded clinical choice.

Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer.

To develop a machine learning (ML) algorithm to predict outcome of primary cytoreductive surgery (PCS) in patients with advanced ovarian cancer (AOC) METHODS: This retrospective cohort study included patients with AOC undergoing PCS between January 2017 and February 2021. Using radiologic criteria, patient factors (age, CA-125, performance status, BRCA) and surgical complexity scores, we trained a random forest model to predict the dichotomous outcome of optimal cytoreduction (<1 cm) and no gross residual (RD = 0 mm) using JMP-Pro 15 (SAS). This model is available at https://ipm-ml.ccm.sickkids.ca. One hundred and fifty-one patients underwent PCS and randomly assigned to train (n = 92), validate (n = 30), or test (n = 29) the model. The median age was 58 (27-83). Patients with suboptimal cytoreduction were more likely to have an Eastern Cooperative Oncology Group 3-4 (11% vs. 0.75%, p = 0.004), lower albumin (38 vs. 41,p = 0.02), and higher CA125 (1126 vs. 388,p = 0.012) than patients with optimal cytoreduction (n = 133). There were no significant differences in age, histology, stage, or BRCA status between groups. The bootstrap random forest model had AUCs of 99.8% (training), 89.6%(validation), and 89.0% (test). The top fivecontributors were CA125, albumin, diaphragmatic disease, age, and ascites. For RD = 0 mm, the AUCs were 94.4%, 52%, and 84%, respectively. Our ML algorithm demonstrated high accuracy in predicting optimal cytoreduction in patients with AOC selected for PCS and may assist decision-making.

Abstract 14347: Rapid Calculation of Cardiology Risk Scores by Natural Language Processing of Unstructured Medical Record

Introduction: Risk score calculators such as ASCVD (for coronary artery disease), CHA2DS2-VASc (for atrial fibrillation), and H2FPEF (for diastolic heart failure) optimize outpatient cardiology care, but may not be routinely performed because calculation is time consuming. Natural Language Processing (NLP) methods can rapidly capture clinical data from unstructured medical records. Our aim was to create an algorithm that can rapidly calculate these scores using NLP methods. Methods: In the algorithm we created (named MYTH), demographic, history and physical, ICD10 diagnostic codes, laboratory, EKG, echocardiography data were captured by analysis of textual syntax, domain-specific knowledge graph, and regular expressions. The data captured are then fed into various risk score calculators. We compared the speed of the algorithm with manual calculation using a commonly used online calculator. Twenty outpatient cardiology unstructured medical records were used for each of the three risk scores. Results: NLP is faster than manual calculation. See attached table. Conclusions: NLP-based algorithm increases the speed of risk score calculation. The improvement is especially dramatic in complex risk scores, such as diastolic heart failure risk scores. NLP method can optimize cardiology care in time-constrained settings.

Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy

PurposeTotal marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces the treatment delivery to be split 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.MethodsTen 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‑V98% > 98%) at the junction region and several dose statistics (D98%, Dmean, and D2%) were compared between automatic and manual plans. The modulation complexity score (MCS) was used to assess plan complexity.ResultsThe automatic procedure required 60–90 min, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J‑V98% > 98%), with significant differences found at the junction region, where Dmean and D2% 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.ConclusionThe developed procedure allowed treatment planning of TMI-TMLI to be streamlined, 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.

Optimal Echocardiographic Parameters to Improve the Diagnostic Yield of Tc-99m-Bone Avid Tracer Cardiac Scintigraphy for Transthyretin Cardiac Amyloidosis.

Echocardiographic deformation-based ratios and novel multi-parametric scores have been suggested to discriminate transthyretin cardiac amyloidosis (ATTR-CM) from other causes of increased left ventricular wall thickness among patients referred for ATTR-CM evaluation. Their relative predictive accuracy has not been well studied. We sought to (1) identify echocardiographic parameters predictive of ATTR-CM and (2) compare the diagnostic accuracy of these parameters in patients with suspected ATTR-CM referred for technetium-99m-pyrophosphate scintigraphy. Echocardiograms from 598 patients referred to 3 major amyloidosis centers for technetium-99m-pyrophosphate to detect ATTR-CM were analyzed, including longitudinal strain (LS) analysis. Deformation ratios (septal apex to base ratio, relative apical sparing, ejection fraction to global LS), a multi-center European increased wall thickness score, and Mayo Clinic derived ATTR score (transthyretin cardiac amyloidosis score) were calculated. A logistic regression model was used to identify the parameters that best associated with a diagnosis of ATTR-CM. Comparison of the diagnostic capacity of the parameters was performed by receiver operating characteristic curves and the area under the curve (AUC). Over half of the subjects (54.2%) were diagnosed with ATTR-CM (78% were men, median age of 76 years). Age, inferolateral wall thickness, and basal LS were the strongest predictors of ATTR-CM, AUC of 0.87 (95% CI: 0.83, 0.90), superior to the increased wall thickness score AUC of 0.78 (95% CI: 0.73, 0.83; P=0.004). An inferolateral wall thickness of ≥14 mm (AUC: 0.73) was as accurate as the published cut-offs for transthyretin cardiac amyloidosis score and septal apex to base (AUC: 0.72 and 0.69, P=0.8 and P=0.1, respectively), and was superior to ejection fraction to global LS and relative apical sparing (AUC: 0.64 and 0.53, P<0.001, respectively). A cut-off of ≥-8% for average basal LS (AUC: 0.76, CI: 0.72-0.79) had a similar area under the curve to transthyretin cardiac amyloidosis score (TCAS) (P=0.2); outperforming the other indices (P<0.01). Inferolateral wall thickness and average basal LS performed as well as or better than more complex echo ratios and multiparametric scores to predict ATTR-CM.

Evaluating the effect of high-density measurement mode on patient-specific quality assurance for head and neck cancer with ArcCHECK.

The high-density measurement (HDm) mode of the ArcCHECK device can achieve a twofold resolution enhancement compared to the standard measurement (Sm) mode. The aim of this study was to evaluate the effect of HDm on the gamma passing rate (GPR) for the patient-specific quality assurance (PSQA) in head and neck cancer. We retrospectively evaluated 30 patients who underwent volumetric modulated arc therapy (VMAT) for head and neck cancer. Absolute gamma analysis was performed on Sm and HDm data. We also investigated correlations between the modulation complexity score for VMAT (MCSv) and differences in the GPR between the two measurement modes. The global GPR of Sm and HDm was 81.0% ± 8.4% and 82.6% ± 7.6% for the 2%/2 mm criterion, 94.0% ± 4.1% and 94.9% ± 3.6% for the 3%/2 mm criterion, and 96.6% ± 2.4% and 97.0% ± 2.4% for the 3%/3 mm criterion, respectively. HDm slightly improved GPR (p < 0.01) for the 2%/2 mm criterion. Differences in GPR between Sm and HDm for the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria were 1.6% ± 3.0%, 0.8% ± 2.0%, and 0.4% ± 1.2%, respectively. No correlation was identified between the MCSv and the difference in GPR between Sm and HDm. Despite an improvement in GPR with HDm, the difference in GPR between Sm and HDm was approximately 2% even when the tighter criteria were used. Moreover, the change in the GPR between Sm and HDm did not depend on plan complexity. Thus, the effect of HDm on GPR is limited for the PSQA in VMAT for head and neck cancer.