bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improvement of the timeliness to lung cancer diagnosis and treatment with the Rapid Access Lung Lesion Clinic in regional Australia.
Authors: Yeoh Hui Ling , Malsem Eve , Harrap Benjamin , McCann Katherine , Faisal Wasek 
Published: June 2023

e13519 Background: The Rapid Access Lung Lesion Clinic (RALLC) was implemented to expedite the multidisciplinary diagnostic pathway of suspected lung cancer in a regional tertiary centre in Victoria, Australia. The aim of this study was to audit the performance of this clinic against the recommended timeframes of the Victorian Optimal Care Pathway for People with Lung Cancer. Methods: Data of all patients referred to the RALLC clinic between July 2017 and December 2021 was prospectively collected, and compared with patients with lung lesions referred to Ballarat Hospital between 2014 to 2015. Information was collected regarding these cohorts’ demographics, cancer care pathway and diagnosis. Results: 302 patients were analysed in this study, of whom, 266 were managed by the RALLC between 2017 to 2021, and 36 patients were managed pre-RALLC between 2014 and 2015. In this cohort, 161 (53%) were males, 256 (85%) were ECOG 0-1 and 251 (83%) had non-small cell lung cancer (NSCLC). 184 (61%) referrals were from general practitioners and 161 (53%) patients received curative-intent treatment. The median time between referral to appointment was 8 days for the RALLC cohort vs. 11 days for the pre-RALLC cohort. The median time between referral to diagnosis was 20 days for the RALLC cohort vs. 36 days for the pre-RALLC cohort. The median time between referral to treatment was 45 days for the RALLC cohort vs. 62 days for the pre-RALLC cohort. The median time between specialist appointment to diagnosis was 11 days for the RALLC cohort vs. 24 days for the pre-RALLC cohort. Conclusions: Although the RALLC did not significantly reduce time between referral to specialist appointment, the clinic did reduce time between referral and specialist appointment to diagnosis, and the time between referral to treatment. The clinic has achieved a marked improvement in meeting the standards of the Optimal Care Pathway for this regional tertiary centre in Australia and demonstrates the benefits of coordinated multidisciplinary service provision especially where specialist access may be limited. Future research is being undertaken to investigate its impact on the clinical outcomes for patients. [Table: see text]
Barriers in the identification and diagnosis of hereditary colon cancer.
Authors: Usry Benjamin , Graf Kevin , Raman Rohith , Allen Caitlin , Meeder Kiersten , Hughes Kevin S. , Donahue Colleen 
Published: June 2023

e13524 Background: Lynch Syndrome (LS), the most common form of hereditary colorectal cancer (CRC), is due to germline pathogenic variant (PV) in mismatch repair genes (MMR). Since 2009, the National Comprehensive Cancer Network (NCCN) recommends MMR screening for LS for all CRCs regardless of age. The American College of Medical Genetics and Genomics recommends patients with MMR deficiency or microsatellite instability (MSI) be referred for genetic testing. Despite these recommendations, compliance is low. The aim of this study was to evaluate compliance with LS screening and genetic testing in CRC. Methods: A single institution retrospective review of patients diagnosed with CRC from 2018-2021 was performed. A chart review of those diagnosed in 2018 and 2021 was analyzed and combined as a single population from pre and post COVID periods. A student’s T-Test was used to compare continuous variables and chi square for categorical variables within the two populations. The compliance with either MSI or MMR screening was calculated. Meeting criteria for genetic testing was defined as age less than 50, MSI-L/H or MMR deficient. These patients were then analyzed to determine the proportion of genetic referrals, genetic counseling, genetic testing and identification of LS. Results: 276 patients with CRC were reviewed, 153 in 2018, 123 in 2021. 71.2% were Caucasian and 58% male. There was no statistically significant difference in gender, race or ethnicity, however, there was a difference in age, with a younger average age in 2021 (67, 63; p = 0.014). 146 (53%) patients were screened for LS. 44 (16%) with MSI, 19 (7%) MMR and 82 (30%) with both. 78 patients met criteria for genetic testing; 47 due to age alone, 6 age and positive LS screening and 25 screening alone. 35% (27/78) of those meeting criteria were referred to genetics, of which 63% (17/27) completed their appointment, 63% (17/27) were tested, and 37% (10/27) had a PV identified. Conclusions: Despite guidelines for LS screening, compliance is low with only half of CRC patients appropriately screened at our institution. Even patients with positive screening and those less than 50 were not referred for genetic testing. However, of referred patients, 63% completed their appointment and all those were tested. This supports the idea that poor compliance with genetic testing is not due to patient compliance, but shows a systems issue at a higher level. To improve LS identification, reflex screening and genetic referrals are paramount. In our study, 59% of patients with germline testing had a PV, however 65% of those meeting criteria were never referred, which could reflect a proportion of patients with LS going unidentified. This validates the 2022 NCCN guidelines recommending genetic testing for all CRCs despite age or MSI/MMR status. By removing the barrier of MMR testing, more genetic testing will likely be done. [Table: see text]
Identifying care fragmentation in the management of non-metastatic muscle-invasive bladder cancer.
Authors: Bhattacharya Manisha , Sun Zhaojun , Jacobs Bruce Lee , Yabes Jonathan G. , Sabik Lindsay M. 
Published: June 2023

e13527 Background: Measuring care fragmentation facilitates exploration of breakdowns in cancer care delivery that may impact quality of care and patient outcomes. Bladder cancer often requires a multimodal approach of surgery, chemotherapy and radiation, increasing the risk of fragmented care. This study assesses the extent of care fragmentation across different inpatient facilities and examines patient characteristics associated with inpatient care fragmentation. Methods: We adapt the modified Herfindahl-Hirschman market concentration index to construct a Fragmentation Index (FI) capturing the distribution of a patient’s care across inpatient facilities. We calculate FI for the 15 months following diagnosis for non-metastatic muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from 2010-2016 and had at least 2 admissions during the study period. We use Pennsylvania Cancer Registry data linked to statewide facility discharge records to estimate patient-level multivariable regression models of the relationship between FI and patient demographics, disease features, rurality, area deprivation index (ADI), payer, and Commission on Cancer hospital (CoC-H) accreditation status. Results: Of 965 patients, 87.3% (n = 842) lived in urban areas, 8.9% in large towns and 3.8% rural. Approximately 8% had Medicaid and 5.7% had uninsured or unknown payer status. A majority (64%; n = 614) were age 60-79; 76% were men and 92% non-Hispanic White; 19.5% (n = 188) had T4 disease and 25% (n = 239) received neoadjuvant chemotherapy. Almost half of patients (45.3%; n = 437) had an FI of 0, indicating all admissions were at the same hospital. The mean FI was 0.254 for the total cohort and 0.464 for patients with FI > 0. Residency in a large town or rural area was associated with 0.59 (CI 0.003—0.114) and 0.14 (CI 0.060—0.220) higher FI relative to urban areas. T4 disease was associated with 0.063 higher FI (CI 0.019—0.107) relative to <T2 disease. Factors associated with lower FI included Non-White race (-0.092, CI -0.151 – -0.033) and no admissions to CoC-H (-0.138, CI -0.182 – -0.095) relative to patients admitted to at least 1 CoC-H. Patients never admitted to a CoC-H (n = 134) mostly lived in urban areas (88%) and 49% had Medicare insurance: they were disproportionately from the most disadvantaged ADI quartile in the cohort (37.3%). Conclusions: Our findings highlight that many MIBC patients receive inpatient care across at least two different hospitals. Advanced disease and patient residence in non-urban areas are associated with greater fragmentation of inpatient care. Patients who never received care at a CoC hospital experienced less fragmentation but were more likely to be relatively economically disadvantaged. Future research will investigate relationships between fragmentation and clinical outcomes to inform policy and clinical strategies aimed at optimizing patterns of care.
Assessing the feasibility of recording smartphone-based patient-reported outcomes in patients with cancer: A pilot study.
Authors: Batra Atul , Sharma Atul , Bakhshi Sameer , Gogia Ajay , Pramanik Raja , Khurana Sachin , PUSHPAM DEEPAM , kumar akash , Sharma Aparna 
Published: June 2023

e13528 Background: We conducted this study to evaluate the feasibility of completing patient-reported outcomes (PROs) using a mobile-based secure system at home, as opposed to the traditional method of completing them in crowded outpatient oncology clinics during hospital visits, which may not be ideal in resource-limited settings. Methods: The study included patients aged over 18 years who were newly diagnosed with solid-organ cancer between July 2021 and July 2022 at a tertiary cancer center in India. Patients who were able to use a smartphone were invited to complete a mobile-based Edmonton Symptom Assessment Scale (ESAS) questionnaire, which was accessed via a secure link sent to their phone as a chat. The ESAS questionnaire included physical (six domains: pain, tiredness, drowsiness, shortness of breath, nausea, loss of appetite), psychological (anxiety and depression), and overall well-being (one domain) questions, each rated on a scale of 0-10, with a higher number indicating greater symptom burden. Symptoms were classified as mild (1-3), and moderate to severe (4-10). The primary objective was to determine the completion rate of the questionnaire, while the secondary objectives were to determine the incidence of moderate to severe symptoms, both physical and psychological, at cancer diagnosis. Multivariate logistic regression analysis was used to identify factors associated with moderate to severe symptoms. Results: We reached out to 707 consecutive patients who had recently been diagnosed with solid-organ cancer and used smart phone. The median age of participants was 53 years (with an interquartile range of 43-62 years), and 52.3% were female. Breast cancer (30.6%) was the most common diagnosis, followed by lung cancer (28.8%). Approximately, one-third of all patients had metastatic disease at diagnosis, while others were similarly distributed in stage I-III. Overall, 650 patients (91.9%) patients completed the mobile-based questionnaire; 38.9% of patients had moderate to severe physical symptoms and 30.9% had moderate to severe psychological symptoms. Pain (57.7%) and tiredness (58.7%) were the most commonly reported physical symptoms in moderate to severe category, while nausea (18.1%) and drowsiness (20.3%) were reported least frequently as moderate to severe. Anxiety (35.1%) was more prevalent than depression (26.1%). The total symptom score was mild in 76.5% of patients and moderate to severe in 18.8%. On multivariate logistic regression, patients with advancing age, female sex, and metastatic disease at diagnosis were more likely to report moderate to severe symptoms. Conclusions: Using smartphone-based PROs can be an efficient way to record symptoms in cancer patients, and their high completion rates make them suitable for routine use in oncology clinics, especially considering the increasing number of smartphone subscribers globally.
The availability of comprehensive care of gastric cancer in developing countries: Georgian experience.
Authors: Esakia Tamar , Abuladze Mariam , Maglakelidze Marina , Tsiklauri Khatuna , Sharikadze Nino , Cholikidze Tamta , Rukhadze Tamari 
Published: June 2023

e13529 Background: The availability of comprehensive cancer care in different regions is the main problem in developing countries. In fact, the cancer centers are centralized and patients fail to receive the proper medical care in regions. The situation is critical for patients living in the zones of conflict. In Georgia we have cancer centers only in two cities Tbilisi and Kutaisi. The aim of the study was to investigate the rate of gastric cancer diagnoses in different regions of Georgia, that could be a model for developing countries. Methods: The spread of gastric cancer in Georgia was investigated retrospectively by analyzing the cases reported in different regions based on 5 years national cancer registry data from 2015 to 2021. The data included the number of cases, age, sex, and stage of gastric cancer. Data from all regions of Georgia were analyzed including occupied regions as Abkhazia and Tskhinvali. Other regions were – Tbilisi, Adjara, Guria, Imereti, Kakheti, Mstkheta, Racha, Samtskhe, Kvemo Kartli, Shida Kartli. Results: A total of 1825 patients with gastric cancer were evaluated during the period of 2015-2021 years. The highest number of diagnosed cases were reported in Tbilisi, with 258 cases in males and 200 cases in females, followed by Imereti (Kutaisi) with 125 cases in males and 80 cases in females and Adjara 132 cases in males and 56 cases in females. The other regions had lower numbers of diagnosed cases, with the lowest being Tskhinvali with only three cases in males and six cases in females. In Abkhazia were 44 cases in males and 19 cases in females. In terms of stage distribution there was no difference between the regions, the majority of patients were stage IV accounting 42 % of all cases, 28% was stage III, 10 % was stage II, 5% was stage I and 15% was of unknown stage. Age ranges were different according to the sex and regions – in Tbilisi 35-83 in males and 26-94 in females, in Adjara 29-88 in males and 28-84 in females, in Abkhazia 35-83 in males and 41-83 in females, in Guria 37-91 in males and 39-83 in females, in Imereti 37-88 in males and 34-84 in females, in Kakheti 34-85 in males and 37-89 in females, in Mtskheta 34-91 in males and 50-84 in females, in Racha 31-72 in males and 30-81 in females, in Samegrelo 33-96 in males and 21-90 in females, in Samtskhe 43-94 in males and 38-81 in females, in Kvemo Kartli 26-85 in males and 38-90 in females, in Shida Kartli 39-85 in males and 38-90 in females, in Tskhinvali 66-84 in males and 33-76 in females. Conclusions: The study found that availability of comprehensive care for gastric cancer including diagnosing is limited in the regions of Georgia, with the lowest rates in occupied regions. To improve the cancer care in developing countries such as Georgia international and national strategies are needed.
Addressing suicidal ideation in oncology patients.
Authors: Sandow Lyndsey , Friedman Ryan , Agrawal Neha , Graff Julie N. , Thawani Rajat 
Published: June 2023

e13534 Background: The diagnosis of cancer leads to worsening a patient’s physical, mental, personal stressors and inevitably can lead to patients questioning their mortality. Cancer is the leading cause of death in the United States, and the diagnosis and treatment of cancer can be very difficult for patients from a physical, psychosocial, and financial perspective. Suicide is the 10th leading cause of death in the United States and risk factors for suicide include family history of suicide, previous suicide attempts, feelings of hopelessness, and depression. In addition to these important risk factors, a recent study including 8.6 million cancer patients showed that risk of suicide is four times greater in cancer patients than the general population. Despite this elevated risk, the detection of cancer patients at highest risk of suicide is poor and there are limited resources to help providers risk stratify cancer patients at highest risk of suicide. Methods: We conducted a cross sectional study of Portland VA oncology providers (physicians, housestaff, advanced practice providers, nurses and social workers) to determine the frequency in which those providers work with oncology patients struggling with suicidal ideation, the frequency of screening completed by the oncology providers, and the barriers to screening patients for suicidal ideation during a regular office visit. Qualtrics surveys were sent to all VA oncology providers. A significance test for proportions was utilized to determine the statistical validity of the results. Results: A total of 22 providers completed the survey. The majority of respondents were MD/DOs (n=13 We showed that most providers have cared for patients affected by suicidal ideation including attempted suicide and completed suicide attempts (p < 0.05). While most providers answered that they had screened patients for suicide before all respondents noted significant barriers to screening patients. The majority of respondents identified multiple barriers to addressing suicidality with almost all respondents included “not enough time” as one of the barriers. Other prominent barriers included, “unsure of next steps”, “not having proper tools to assess”, “not having expertise to assess”. Conclusions: Our study showed that most oncology providers will care for patients affected by suicidal ideation during their career. In addition it shows that while many providers have screened patients for suicidal ideation, most providers face significant barriers to doing so during a clinic visit. Our study contributes to the current body of knowledge by identifying the significant barriers faced in the providers in the Portland VA oncology clinic.
A low-cost breast cancer prognosis tool for developing countries using AI.
Authors: González Espinoza Iván Romarico , Castro-Ponce Abraham , Jiménez-Monferrer Sergio , Castelló-Pons María , Cardona-Ferrer Edgar E. , Juárez-Salazar Gabriela , Juárez-Salazar Diego A. , Montes-de-Oca-Moreda Ricardo I. , Coyotl-Huexotl Marisela , Chiquillo-Domínguez Mariana , Acosta Ponce de León Eric , Aguilar Priego José Manuel , Cordero Vargas Carlos , Cortes Garcia Carlos Alberto , Ceja-Utrera Francisco J. , Lara-Bejarano Juan C. , Gonzalez Blanco María de Jesús , Garibay Diaz Julio Cesar , Aranda Claussen Jorge E. 
Published: June 2023

e13536 Background: The use of artificial intelligence in breast cancer diagnosis has had a significant impact. Alternative to other tests, machine learning (ML) models are low-cost tools that can establish an objective prognosis by considering all the clinical, genomic, and histological data from patients. To evaluate the performance of 12 ML models in predicting the overall survival of breast cancer patients at 60 months of follow-up. Methods: Clinical data was obtained from “The Cancer Genome Atlas-Breast Cancer" database. Preprocessing of data ruled out variables with insufficient values or poor relevance. Data was divided into an 80/20 ratio to perform the training and testing of the models. The models used were: Logistic Regression (LR), Ridge Classifier (RC), Least Absolute Shrinkage and Selection Operator (LASSO), K-nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Tree (DT), Multilayer Perceptron (MP), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). Initial training used the default hyperparameters of each model, performance was evaluated through a 5-fold-cross-validation, the results were used with the GridSearchCV tool to optimize the hyperparameters for the final training and testing of the models. Results: The models with most accuracy were NB (86.76%) and SGD (85.29%), with most sensitivity were: SGD (93.75%), MP (91.67%) and NB (89.89%), with the most specificity were: KNN (95.0%), DT (90.0%) and LASSO (85.0%), with the highest area under the ROC curve were: LDA (90.83%), LASSO (90.1%), and LR (89.79%). The variables with the highest relevance were: "Previous diagnosis of cancer", "Presence of tumor", "Ancillary Therapy", and "Histology". Integrating the best-performing models into an interactive tool preserves the best features without sacrificing efficiency. Conclusions: The models with the best overall performance in predicting the prognosis of patients with breast cancer were NB and LASSO. The best way to evaluate the performance of a predictive prognosis tool is the area under the ROC curve. For developing countries, an AI tool to predict the patient outcome is viable when other expensive prognosis genomic tools are not available. Further studies with data from developing countries are needed to improve the performance of this tool.
Guidance on modeling circulating miRNA to distinguish multiple cancer types by an observation of large-scale open data.
Authors: Hsieh Jason Chia-Hsun , Hsieh Tsung-Ting , Lee Ko-Han , Chang Yu-Chuan 
Published: June 2023

e13537 Background: Cell-free miRNAs (cf-miRNA), circulated in body fluids such as plasma or serum, have shown their ability to detect, diagnose, and monitor cancers. Combining machine learning (ML) technology with these biomarkers facilitates early detection of cancers, which increases the accuracy of clinical decisions and empowers people to take control of their health status. However, the data of the cf-miRNAs has characteristics, which will affect the results of ML. Therefore, this study tries to expound on them in different aspects and to build a reasonable model. Methods: We downloaded large-scale datasets of the platform GPL21263 from the Gene Expression Omnibus for modeling experiments. We curated 8,174 subjects with 2,565 miRNA targets across 7 cancer types of different cf-miRNA-based cancer studies. Moreover, we used principal component analysis (PCA) to observe the datasets, recursive feature elimination (RFE) for feature selection, and tree-based algorithms to build the prediction model. Results: The characteristics of the cf-miRNA we like to share are: (1) Cancer subjects express more cf-miRNAs than control subjects. In the control group, there were 294 and 327 miRNAs with missing rates under 50% and 25%, respectively. In contrast, there were 395 and 485 miRNAs with the same thresholds in the cancer group; (2) Dividing subjects into cancer and control groups is simpler than distinguishing specific cancer types. In the PCA, the average Euclidean distance between the control group and each cancer type is 98.48, while it is 20.23 within each cancer type; (3) For obtaining cancer-specific biomarkers, we suggested that other non-target cancer subjects should be considered as negative controls. We modeled 7 cancer types and compared the proportion of cancer-specific biomarkers, not selected by any other models. The proportion increased from 30.0% to 57.8% after we added other non-target cancer subjects to the control group; next, we focus on multi-cancer modeling: (4) We need at least 400 samples to distinguish seven cancer types. In our experiment, we kept increasing the size of the training data, a hundred at a time. As data was added to the model, the accuracy increased but plateaued after adding 400 samples; (5) Based on RFE, 120 miRNAs is a reasonable number to distinguish seven cancers. Moreover, we found some of these miRNAs are only expressed in cancer subjects. We might lose this kind of biomarkers if we filtered out them by the missing rate; (6) The 10-fold cross validation accuracy of the multi-cancer model can achieve 93.0% using the gradient-boosted trees algorithm. Conclusions: In this study, we showed the guidance for modeling miRNAs in different aspects, including labeling strategy, sample and feature sizes, and the high-accuracy multi-cancer model we can achieve. We hope this guidance would inspire researchers on cf-miRNA-related machine learning applications.