bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel risk-scoring model developed based on tertiary lymphoid structure related genes predict the prognosis and immunotherapeutic efficacy in cutaneous melanoma.
Authors: Wang Bin , Liu Jie , Jiang Yu 
Published: June 2022

e13574 Background: Emerging evidence has proven that tertiary lymphoid structures (TLSs) are predictive biomarkers of favorable clinical outcomes in melanoma, whereas the exact role of TLS-related genes remained unclear. In the present study, we aimed to establish a novel risk-scoring model based on TLS-related genes to predict the prognosis and immunotherapeutic efficacy in cutaneous melanoma. Methods: Patients in the TCGA-SKCM cohort (n = 468) were clustered into two subtypes based on the prognosis-related genes associated with TLSs though consensus clustering analysis, and differentially expressed genes were screened between two clusters. By univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 13-gene signature was selected to construct a prognostic risk-scoring model. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups. GSE65904 (n = 220) and GSE544467 (n = 79) cohorts were used for external validation. Univariate and multivariate cox analysis were used to select significant variables for construction of nomogram. The nomogram model was evaluated by concordance index, time-dependent receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA). GSEA, CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS) were utilized to explore the potential mechanism of immune-related process and immunotherapy. Results: Thirteen signature genes (CCL8, DLX3, CNTFR, CNFN, BCAN, CD33, HAPLN3, ABCB1, CLIC2, GCNT1, FGF1, A2ML1, GBP2) were developed to construct a risk-scoring model. Melanoma patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.76, 0.72, and 0.73 in the training set, respectively, indicating a good predictive efficacy. In addition, based on independent prognostic factors including risk score, age, stage, T and N Staging after univariate and multivariate Cox regression analyses, a nomogram was constructed to better estimate individual survival. For the prediction of 1, 3, and 5-year overall survival probability according to the nomogram, the area under the receiver operating characteristic curve was 0.81, 0.80 and 0.76, respectively. Furthermore, we explored the correlation of the prognostic risk-scoring model with tumor microenvironment (TME) and immunotherapeutic efficacy. Patients in the low risk group featured immunoactive TME, higher IPS scores and lower TIDE scores, and was regarded as the potential beneficiaries of immunotherapy. Conclusions: The 13-gene risk score proposed in the research may provide new insights into the individualized evaluation of cutaneous melanoma prognosis and Immunotherapeutic Efficacy
Diagnostic accuracy of the CxPREVENTIVE breast model for early detection of breast cancer.
Authors: Calleja Sergio J , Patiño Édgar C 
Published: June 2022

e13578 Background: Despite different available methods for breast cancer (BC) screening (mainly mammographies for ages between 50 and 65 yr.), and their proven benefits, morbidity, and mortality of this malignancy are still high, partly due to technical limitations and low compliance with screening. The aim of the present study was to assess the diagnostic accuracy for early detection of BC using a modeling algorithm based on multiple blood and urine biomarkers. Methods: In this retrospective cohort study, clinical and blood and urine markers-based algorithms with predefined cutoff values were applied to a generally healthy asymptomatic outpatient cohort of women aged 23 years or older undergoing mammography and histopathology for routine breast cancer screening. We used a modeling algorithm based on two different frameworks (Multiple Biomarkers Disease Activity Algorithm MBDAA [Bioprognos], and Artificial Intelligence Recursive Algorithm AIRA [Mintlot] both in Barcelona, Spain). Both algorithms compute information from a wide number of analytes, tumor markers (CA 15.3, CEA, EGFR, NGAL, NSE and 8-OHdG), and additional clinical data (age, weight, height, and BMI). MBDAA includes Barcelona Criteria (four criteria to correctly interpret the cause of the increase in tumor markers, benign or malignant, defined by the Spanish Society of Clinical Biochemistry and Molecular Pathology, Commission of Biological Markers of Cancer). To assess the association between the modelling algorithm and downstream diagnosis we used logistic regression to compute receiver operating characteristic curves (ROC). Exact binomial confidence limits were calculated to test accuracy, sensitivity, and specificity. Results: 123 women (median age 48 yr. [IQR 40-63]) who underwent mammography at the Juan Ramón Jiménez Hospital (Huelva, Spain), were included in this analysis. Based on mammography and histopathologic findings, BC was diagnosed in 61 (49.5%) women, and absence of pathological findings (control group) was seen in 62 (51.5%). The diagnostic intervention for BC yielded an area under the curve (AUC) of 1 among the entire specimen set, accuracy 1 (95% CI 0.990,1), Se 1 (0.991, 1) and Sp 1 (0.991, 1). Conclusions: Using mammography as a gold standard, our data suggest that an innovative newly designed non-invasive blood and urine-based biomarker algorithm CxPREVENTIVE Breast may provide a timely diagnosis of BC with very high accuracy, especially for those women under 50 and over 65yr., where up to 60% of all BC are concentrated. Our findings warrant further investigation, and we are planning large-scale (n 10,000), fully powered study to inform clinical practice.
Canadian oncology residents’ knowledge of and attitudes towards artificial intelligence and machine learning.
Authors: Kennedy Thomas , Collie Laura , Nabhen Jacqueline J. , Ynoe de Moraes Fabio 
Published: June 2022

e13587 Background: The use of artificial intelligence (AI) and machine learning is becoming more common and is expected to expand further in order to meet the needs of our ever-evolving healthcare system. In oncology, AI and machine learning are already being explored in various applications. Despite AI’s importance, there is sparse formal teaching on AI incorporated into medical schools’ curricula and residency training programs. In this study, we examined the perceptions and knowledge of Canadian oncology residents and fellows with respect to AI technologies. Methods: An electronic, anonymous, questionnaire-based survey was distributed to residents and fellows in medical and radiation oncology programs across Canada. Survey questions spanned areas of demographics, familiarity with AI, personal attitudes towards AI, and perspectives regarding AI use in different specialties. Approval was obtained from the Queen’s Research Ethics Board prior to conducting this study. Mixed-methods statistical analysis is ongoing. Qualitative data will be analyzed using thematic analysis. Univariable and multivariable regressions will be conducted to identify any correlation between perception or knowledge of AI and demographic factors. Results: Fifty-seven participants responded in total. Most residents (67%) agreed or strongly agreed that it was important they learn about AI. Seventy percent indicated that, if given the chance, they would like to learn more about AI, yet the majority of participants (88%) indicated they had not received formalized teaching. Disciplines that were felt to be most associated with AI were radiology (98%), radiation oncology (84%), and pathology (58%). With respect to the field of radiation oncology, 98% of respondents felt that AI had the potential to replace some, most, or all medical activities. A perceived barrier to understanding AI was a lack of knowledge of mathematics and programming (63%). Respondents indicated that their preferred formats for learning about AI would be workshops (78%), lectures (60%), and collaborative activities with other departments (46%). Conclusions: Our results show that Canadian oncology residents’ sense that AI is important and relevant to their area of training. Despite this, they have not received education on these topics. Thus, formalized teaching, such as lectures and workshops, would be perceived as beneficial by most Canadian oncology residents.
Reliable determination of genetic ancestry from cancer-derived molecular data.
Authors: Belleau Pascal , Deschênes Astrid , Tuveson David A. , Krasnitz Alexander 
Published: June 2022

e13588 Background: There is ample epidemiological evidence that, for multiple cancer types, incidence, stage at presentation and outcome correlate strongly with the patient’s race or ethnicity. Recent examination of cancer data repositories such as TCGA has revealed has revealed, for a number of cancers, ancestry dependence of somatic mutation spectrum and transcriptional phenotype. Further progress in genetic ancestry-oriented cancer research requires the ability to perform accurate and robust ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Such inference is potentially challenging, given widespread somatic alterations in tumor genomes, observed in multiple types of cancer. Methods: In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we develop a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. We then infer the substitute individual’s ancestry, using well-established methods of population genetics and relying on the 1000 Genomes collection as a population reference data set. Data synthesis is applicable to multiple profiling platforms and makes it possible to assess the performance of inference specifically for a given molecular profile, and separately for each continental-level ancestry. This ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. Results: We test our approach for three representative cancer types: pancreatic and ovarian cancers as representative of epithelial tumor types, and acute myeloid leukemia as an example of hematopoietic malignancy. We consider tumor-derived data acquired by the whole-exome sequencing, by exome sequencing restricted to a panel of cancer-associated genes, and by RNA sequencing. For all three cancer types, and across the three sequencing platforms, we demonstrate that global, continental-level ancestry of the patient can be inferred accurately and robustly. Specifically, we find the inferred ancestries to be in at least 97% agreement with the golden standard of the ancestry derived from the matching cancer-free genomes. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Conclusions: Our study demonstrates that vast amounts of existing cancer-derived molecular data are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients' self-identification by ancestry. Furthermore, the procedure we developed facilitates ancestry-oriented research using archived tumor tissue specimens, for which matching cancer-free specimens may not be available.
Characteristics and outcomes of COVID-19 in adult cancer patients: Tertiary cancer center experience.
Authors: Ababneh Hazim , Alqadi Fadwa , Ma'koseh Mohammad , Halahleh Khalid , Abo abed Layan , Akkawi Mohammad , Al-Rabi Kamal , Abdel-Razeq Hikmat 
Published: June 2022

e13601 Background: The COVID-19 pandemic has led to deteriorated outcomes among immunocompromised patients, significantly impacting cancer patients. However, little is known about the impact of the COVID-19 pandemic on cancer patients in low and middle-income countries. Therefore, we sought to investigate the characteristics and clinical outcomes of cancer patients with COVID-19 treated at a single institution. Methods: A retrospective chart review was performed for cancer patients treated at King Hussein Cancer Center. Patients who were diagnosed with laboratory-confirmed SARS-CoV-2 infection by Real-time Polymerase chain reaction test between April 2020 and October 2020 were identified. Results: Overall, 327 COVID-19-infected cancer patients were included. At the time of COVID-19 diagnosis, the median age was 55 years (range, 18-87 years), 146 patients (45%) were males. The most common neoplasms were breast cancer (n = 90, 27.5%), gastrointestinal cancers (n = 60, 18.3%), and lymphoma (n = 36, 11%). The majority of patients had comorbidities (n = 200, 61%), of which hypertension and diabetes mellitus were the most common. Testing reason was presence of symptoms in 183 (56%) patients, previous exposure in 10 (3.1%), and before elective procedure in 142 (43.4%). 118 patients (56.5%) were hospitalized and 18 patients (5.5%) required admission to the intensive care unit (ICU). At the time of the last follow-up, 76% (n = 249) remained alive, and 24% (n = 78) died, among which death was considered to be COVID related in 41 (52.5% of deaths). Mortality was significantly increased in patients with comorbidities (29%, vs 15.7 %; p = 0.008), the use of cardiac medications (34.3% vs. 18.1%; p = 0.001), active cancer status vs. remission (28.2% vs 11.6% p = 0.002), receiving chemotherapy in the last four weeks (27.6% vs 16.4%, p = 0.028), and when testing reason was the presence of symptoms vs exposure and elective (37.7% vs. 10% and 7.7% p = < 0.001) respectively. Chemotherapy was delayed in 109 (33.4%) patients and permanently discontinued in 57 (17.5%) patients. Among patients required hospitalization and needed ICU admission, mortality rates were 55.1% and 88.9% respectively. Conclusions: COVID-19 infection is associated with significant mortality and negatively affects treatment plans in cancer patients. A follow-up study will be needed to evaluate the effect of vaccination on the outcomes.
Improving quality of life by promoting patient-provider communication and optimizing social support using a digital health platform.
Authors: Saria Marlon Garzo , Kesari Santosh 
Published: June 2022

e13622 Background: The patient experience, including symptom burden, is often disease-specific and dependent on host factors, the environment, and the tumor itself. Patients with a previous diagnosis of breast cancer are often subjected to multiple and aggressive treatment modalities compounding the already existing symptom burden related to the disease. Digital health can potentially increase patient engagement, close communication gaps between patients and providers, improve providers’ ability to stratify patient populations and adapt services, enable patients and families to access convenient and cost-effective care, and improve decision-making by patients and providers. The primary purpose of this pilot study was to evaluate if the use of a digital health platform can improve quality of life in patients with breast cancer. Methods: Patients with diagnosis of breast cancer were enrolled in a nonblinded, randomized, controlled trial of a digital health platform in self-reporting and monitoring of cancer and cancer-treatment related symptoms compared with usual care. All participants were asked to register in an online mobile application called CancerLife. This novel cancer social network experience provides patients social support through their care journey while at the same time collects patient-reported outcomes displayed inside reports that they can share with their providers. CancerLife automatically randomized each participant and assigned them to the experimental or control arms. The control arm received usual care provided for in the clinics. This group did not have access to the social network, reports or any other intervention or digital tools associated with CancerLife. Participants were asked to self-report their quality of life using the European Quality of Life Survey (EQ-5) every three weeks. Participants in the treatment arm were asked use the CancerLife at least 1 times per week for up to 24 weeks. Participants were also asked to share their print-out report directly with their care team and were instructed to email the report to their care team via their patient portal. Welch’s Two Sample t-test was performed to measure any statistical difference in the mean scores within each group for each time period. Results: CancerLife users had significant improvements in their scores at 9, 12, and 18 weeks; the improvement at 15 weeks was not significant. By contrast, the control group did not have significant improvements in their scores in any of the time periods. Conclusions: Breast cancer patients who were users of CancerLife’s app have more improvement in their Quality-of-Life Scores than randomly assigned non-users. The improvements seen with the use of CancerLife center on two things: improving communication from patient to physician about symptoms and offering patients social support which creates emotional resiliency. Clinical trial information: NCT03094741.
Recurrent venous thromboembolism in cancer patients treated with direct oral anticoagulants.
Authors: Talbot Alice , Dean Andrew Peter , Brennan Ciara , Talbot Thomas , Angelov Sophia 
Published: June 2022

e13648 Background: Malignancy associated thrombosis places a significant clinical and financial burden on all major oncology services worldwide. Cancer patients are at an increased risk of thrombosis and large studies have suggested a subsequent increased mortality. (1) Some studies have recorded higher risk associated with certain chemotherapy regimens such as platinum-based agents and gemcitabine. (2) Guidelines for anticoagulation often recommend low molecular weight heparin (LMWH) as first line for malignancy associated thrombosis. (3) In recent years ASCO guidelines have added DOACs as an option for VTE treatment. (4) We herein describe eight cases of recurrent venous thromboembolism despite direct oral anticoagulation. Methods: Data was gathered from electronic patient records for cancer patients attending a single centre in Western Australia between 2016 and 2021. Radiology and medication records were used to confirm VTE diagnoses and subsequent treatment. This data was recorded on an excel file and analysed accordingly. Results: We identified 8 patients with recurrent VTE on DOACs. Median age of patients was 61 years (range 50-72). The most common associated malignancy was pancreatic cancer (n = 3). Other associated cancer types were ovarian (n = 2), lung adenocarcinoma (n = 2) and colorectal cancer(n = 1). Most patients had metastatic disease at time of first VTE(n = 5). The most common site of thrombosis was in the upper limb with 3 cases of initial VTE and 4 cases of recurrent VTE. All of these occurred in patients with peripherally inserted central catheters. (PICC) One patient had a left central retinal vein thrombosis resulting in complete loss of vision in that eye. All but one patient had received either platinum based chemotherapy or gemcitabine. Median time to initial thrombotic event was 4 months from diagnosis. Median time to recurrent thrombotic event was 3.5 months. Rivaroxaban was the most common DOAC used with initial VTE (n = 5). All other patients received apixaban (n = 3). All patients were switched to either LMWH (n = 6) or unfractionated heparin (n = 2) following recurrent VTE. 75% of patients did not have further thrombosis after switch to heparin. One patient had chronic thrombosis on subsequent scans and another patient had recurrent pulmonary embolism despite unfractionated heparin and vena cava filter insertion. Conclusions: Venous thromboembolism is a common complication of malignancy with significant associated morbidity and mortality. A high index of suspicion is needed in patients with clinical signs of recurrent VTE while on a DOAC, especially for those with PICCs. While DOACs are common for patient preference, the differing rates of VTE recurrences should be further investigated and considered in oncology prescribing practice going forward.
Defining the optimal millimeter threshold for target lesion inclusion in the Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) based on outcome prediction.
Authors: Gidwani Mishka , Chang Ken , Patel Jay Biren , Kim Albert Eusik , Gerstner Elizabeth R. , Huang Raymond Yi-kun , Kalpathy-Cramer Jayashree 
Published: June 2022

e14003 Background: The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) is the standard for therapeutic response assessment in brain metastases (BM) patients. The criteria relies on a human reader to annotate target (≥10 mm) and non-target (<10mm) lesions of interest based on unidimensional measurement of lesion diameter on brain MRI. The change in the sum largest axial diameter of 5 target lesions (RANO-BM diameter) is then used to categorize intracranial response into 4 categories: complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). Methods: Automating the RANO-BM criteria (AUTO-RANO-BM) has enabled the rapid modulation of the threshold diameter required for inclusion of a brain metastasis as a target lesion. In this study, diameters below the RANO-BM 10mm recommendation were utilized when calculating the RANO-BM diameter. The ability of the RANO-BM diameter to predict overall survival for BM patients was then evaluated in a Cox Proportional Hazards model. In a cohort of 175 immunotherapy-treated BM patients, the optimal threshold for the RANO-BM diameter as measured by Concordance Index (C-Index) was 5mm, suggesting that the RANO-BM criteria should be amended.