bluuebunny/crossref_metadata_2025_split huggingface dataset:
mICKEY: Memory-efficient deep learning for personalized biomarker discovery and cancer origin prediction from DNA methylation data.
Authors: Jantrachotechatchawan Chanati , Tussanapirom Pakanan , Aewsrisakul Kasidech , Leephatarakit Natthawadee , Duangrattanalert Kobchai 
Published: August 2023

109 Background: Carcinoma of unknown primary (CUP) accounts for up to 5% of all cancer cases and presents challenges in identifying primary cancer sites and successful treatment. DNA methylation abnormalities on the 5'-cytosine-phosphate-guanine-3' (CpG) motif across the genome are associated with carcinogenesis, enabling their use as cancer biomarkers for early diagnosis and tissue origin prediction. This study proposes a Combined Approach for 1) CpG site selection, 2) predicting cancer origin and identifying biomarkers, and 3) developing an open-source application for user-friendly data analysis. Methods: To emulate the state-like progression of cancer and accommodate missing values, discretized methylation beta values and missing values were tokenized. Feature selection using L1 regularization of lambda 0.0003 resulted in 152 CpG sites out of an initial set of 312,792. Three independent sets were selected for each of the 3 self-attention type: vanilla scaled dot-product, dense synthesizer, and factorized dense synthesizer. Self-attention enhances the biomarker discovery approach toward personalization as it generates a unique attention map for each sample input that highlights essential features for primary site prediction. A permutation test further evaluated contribution by the selected CpG sites, confirming biomarkers identification for each primary site. An open-source application was developed to predict cancer origin using methylation beta values, providing a user-friendly interface displaying predicted primary sites and corresponding percentages. Results: Independent sets of selected CpG performs comparably to the initial set with robustness (most well above 97% in precision, recall, and F1) across different clinical (sample type: normal, primary, metastatic, recurrent; and AJCC pathologic stage) and demographic groups (age, gender, race). The model identifies important biomarkers previously shown in literatures i.e. GATA4 and HOXD. Conclusions: This study offers valuable insights into feature selection, primary site prediction, and biomarker discovery using DNA methylation data, with potential practical applications for healthcare facilities and in personalized cancer treatment.
Systematic mapping of gender disparities in oncology publications of north African countries: The GEORGiNA study.
Authors: El Bairi Khalid ,   GEORGiNA study collaborators
Published: August 2023

112 Background: Gender Inequities in cancer academic research continue to be a challenging global issue, including in low- and middle-income countries. While North African research environment has been historically perceived as “male-dominant”, research on gender inequity in this region is largely lacking. GEORGiNA (“Gender Equity in Oncology Research” Group in North Africa) is a bibliometric study focusing on gender disparities aiming at portraying the current situation and inform policy-making to deliver recommendations for gender equality. Methods: A bibliometric analysis based on Pubmed was conducted for the period 2018-2022. Eligible articles included at least one author from a North-African country as first or last position. Cancer researchers were asked to review the articles with the first/last author from their same country of origin, to accurately determine authors’ gender. A manual data extraction guide with simulation was developed and shared with all collaborators to harmonize data extraction, accuracy, and reporting. Factors associated with gender distribution were studied as appropriate. Results: A total of 4,414 articles published by North-African cancer researchers in the last 5 years were included in the final analysis. Females represented 52.5% of first authors, with a notable women’s presence in Tunisia (72.6% vs 27.4%). Morocco, Algeria, and Egypt reported fair distribution in this authorship (ratios ≈0.5) while Sudan and Libya had male dominance (62% and 70%, respectively). Among 23,984 of coauthors, 45% were females and 19% of all publications had no female co-authors. Last authorships were significantly more common for male researchers, in all countries (p < 0.001). Of interest, women’s lead authorships appeared markedly increased over last years, including during the period of COVID-19 outbreak. Original articles presented more commonly last authors who were men (62.6% vs 37.4% women) (p < 0.001). Indeed, male last authors had received more funding than females and published more open access research (p = 0.018). Conversely, disclosure of the funding source was more common for original contributions led by women as first authors (p < 0.001). Male authors (first and last positions) were more involved in international collaborations as compared to female authors in similar positions (p < 0.001). There was association between a reduced number of female co-authors and men in last authorship positions (p < 0.001). On the other hand, when women were first authors, female co-authors resulted substantially higher (p < 0.001). Conclusions: Women in North-African cancer research publications seem to be fairly positioned, albeit differences still exist. Lack of funding and limited engagement in international collaborations are still prevalent barriers for women scientists. The preliminary findings from the GEORGiNA study can catalyze progress toward gender equity.
Explainable machine learning to inform real-world evidence: A case study on small cell lung cancer survival analysis.
Authors: Marzano Luca , Darwich Adam S. , Dan Asaf , Tendler Salomon , Raghotama Jayanth , Lewensohn Rolf , De Petris Luigi , Meijer Sebastiaan 
Published: August 2023

113 Background: In recent years, machine learning algorithms for survival analysis have been developed as an alternative to traditional Cox regression. Despite the good performances, these are viewed as black box models that lack interpretability, which may limit their clinical applications. There has been a growing interest in explaining and interpreting machine learning models, which has led to the emergence of the field of explainable artificial intelligence (XAI). However, few studies have focused on how these methods could be of benefit to survival analysis of real-world healthcare data. Methods: In this study, we explored the use of current XAI techniques to analyze the effects of platinum doublet chemotherapy on small cell lung cancer patients in developed machine learning models.The analyzed data included real-world patients treated at Karolinska University Hospital (n=570) and three phase III randomized clinical trials shared by the Project Data Sphere initiative (n=987). The real-world data covariates included were age, sex, TNM staging, ECOG performance status, lab values, brain metastasis, and concomitant radiotherapy. The aggregated dataset including the clinical trials contained the following variables: age, sex, performance status, brain metastasis, and protocol violations. Eight machine learning models were trained and compared with Cox regression. The performance of the models was evaluated using C-index, and the time variation of Brier Score and C/D AUC.Temporal feature importance and partial dependence were used to explore the overall covariate impact on overall survival (Global XAI). Ceteris-paribus and SurvShap(t) were used to investigate the covariate impact for the single patient prediction (local XAI). The models were firstly trained only on real-world data before aggregation with the clinical trials. Results: Ensemble machine learning provided the best performances. XAI techniques showed the potential to increase explainability of survival predictions in function of time. Global XAI showed the time range of the model reliability, trend inversions regarding treatment decisions and covariate importance along the time. Local XAI allowed to test the impact of covariates between long survival patients and the comparison between real-world and clinical trials. Conclusions: Our results demonstrate the potential of XAI techniques applied to survival machine learning and real-world data, thus providing insights into the mechanisms driving model predictions and demonstrate the utility of this approach in clinical research.
Quantification of timeloss within complex tumor therapies.
Authors: Martens Nora , Vogt Katharina , Eichler Ronny , Herzog Moritz , Illmer Thomas , Bornhäuser Martin , Folprecht Gunnar 
Published: August 2023

126 Background: There are various studies on reducing the waiting time of tumor patients before therapy sessions. In this study, we investigated how much waiting time within therapy sessions occurs due to delayed administration of the next drug. In addition, digital documented infusion time of specific drugs were determined to calculate waiting time. Both metrics serve as an indication of the potential to improve the effectiveness and efficiency of the infusion process. Methods: The therapy sequence of preliminary 29 patients in the Outpatient Therapy Center of the University Hospital Dresden was investigated. PDF export of the prescriptions of all cycles of the patient with the documentation of the therapy sequences from BD Cato is performed. In python the extraction of all time stamps and the processing of all start and stop times of the medication administration took place. Based on this data, waiting time between infusions were calculated. In addition, analysis of the total administration time of the specific medication was performed. Results: This revealed a median of 23 minutes of patient waiting time between infusion sequences (n = 29 patients and 1154 measured infusion changes). We compared 7 specific medications to the intended infusion time, taking particular note of the high variance in duration. We postulate due to standard administration protocols that the infusion time variance indicates additional waiting time. Both metrics are only an indication, since other factors besides the waiting time can also influence the time between two infusions. Conclusions: Treatment time for the same therapy protocol varied widely between patients, both in terms of waiting time between infusions and in the duration of infusion compared to the intended infusion time. Both indicate the opportunity to improve efficiency, thus providing more hospital-free lifetime for patients and increase revenue and standardization for treatment centers.
Efficacy of a digital media platform to combat inequities in global oncology education.
Authors: Leyfman Yan , Menon Gayathri P. , Joshi Muskan , Pawar Shubhadarshini , Wilkerson William B. , Balasubramanian Maduri , Van de Kieft Alexandra , Park Chandler H. 
Published: August 2023

132 Background: Social media has emerged as a powerful vehicle for the dissemination of medical information, especially in resource-limited regions, due to its mass global reach. However, 1/3 of social media posts contain misinformation with 76.9% of them containing harmful information leading to adverse outcomes that are a detriment to global public health. To address this, MedNews Week (MNW), a free, digital medical education platform dedicated to combating healthcare misinformation, was developed to contribute to global oncology education through biweekly programming. Its main show, Keynote Conference, features live virtual presentations from oncology’s premier global leaders discussing the latest developments in cancer before a mainstream global audience. The aim of this study was to assess the worldwide reach and impact of this cost-free, virtual oncology education platform. Methods: From January 2022 to 2023, MNW hosted 25 global oncology leaders as Keynote Speakers (Mean H-index=50.09). Viewership, impressions, and outreach data were collected from MNW’s social media accounts and TweepMaps. Data was analyzed to assess MNW’s global reach and engagement and stratified based on engagement sentiment, gender, age group, and occupation. Results: During this period, MNW generated a Healthcare Social Graph Score of 92.77% (top 7.33 percentile of global healthcare influencers) with over 109,128 Twitter impressions and 4,731 engagements reaching 1.9 million accounts globally over 12 months (Table). Most of the followers were from non-healthcare fields with slightly more males than females. Those who engaged conveyed positive sentiments (59.9% likes and 26.2% mentions) contributing to a viewership increase from 72,136 (6 months) to 157,559 (1 year). The platform reached 100 cities and 57 countries of which 16 were of low-to-middle socioeconomic (SE) status. Conclusions: MNW has demonstrated an ability to attract and sustain a diverse, global, growing audience. Given MNW’s continued growth, it provides an innovative model to engage and disseminate medical information to mainstream audiences. The platform’s ability to showcase global leaders to mainstream audiences gratuitously, especially in lower SE regions that have been historically underserved, offers a practical approach to combat educational inequity. Thus, MNW represents a novel approach with promising potential to positively impact global oncology education.[Table: see text]
Dexamethasone free antiemetic prophylaxis for highly emetogenic chemotherapy: A phase III, double-blind, randomized controlled trial.
Authors: Radhakrishnan Venkatraman , Venkatakrishnan Kritthivasan , Jayachandran Perumal Kalaiyarasi , Selvarajan Gangothri , Rajaraman Swaminathan 
Published: August 2023

133 Background: Chemotherapy-induced nausea and vomiting (CINV) is a common side effect of highly emetogenic chemotherapy (HEC). Current guidelines recommend the use of dexamethasone (DEX) for prophylaxis, but the effectiveness and safety of a DEX-free regimen for CINV prophylaxis in HEC is not known. Methods: This was an investigator-initiated, double-blind, phase 3 RCT designed to show the non-inferiority of a DEX-free regimen (olanzapine, palonosetron, and fosaprepitant; OPF) compared to the NCCN recommended DEX-containing regimen (olanzapine, palonosetron, and dexamethasone; OPD). Chemotherapy-naive patients aged 18-80 years receiving single-day HEC were randomized 1:1 to receive either the OPD regimen (olanzapine 5 mg once a day on days 1-4, palonosetron 0.25 mg on day 1, and DEX 12 mg on day 1) or the OPF regimen (olanzapine and palonosetron as above, and fosaprepitant 150 mg on day 1).The primary endpoint was to compare complete response (CR) rates for vomiting (no vomiting and no rescue medication) during the overall phase (start of chemotherapy to 120 h). The secondary endpoints were CR for nausea (ESAS), toxicities (CTCAE), and patient reported outcome measures (PROM) using MDASI. 175 patients in each arm were required show non-inferiority (NI) of OPF regimen compared to OPD regiment with power of 90%, 2-sided alpha of 5%, and NI margin of 15%. Results: Between May 2022-Dec 2022, 350 patients were randomized and 346 received the study interventions (OPD arm n = 174 and OPF arm n = 172). The most common chemotherapy used was adriamycin/cyclophosphamide (66%) followed by cisplatin (27%). CR rates for vomiting for the overall phase were 48.8% in the OPD arm and 79.6% in the OPF arm (P < 0.001), with an absolute difference of -30.8% and 2-sided 95% CI = (-40.31, -21.24) which was within the pre-defined NI margin of 15% and also showed superiority. The DEX-free arm also had significantly higher CR rates for nausea and total control (vomiting and nausea combined) (Table). PROM for fatigue (P = 0.009) and drowsiness (P = 0.002) were more in the OPF arm in the acute phase, and insomnia (P < 0.001) in the OPD arm in the delayed phase. Global Health Status QOL score on day 6 in the OPD arm was 55.36 ± 16.15 and in the OPF arm was 57.84 ± 12.76, P = 0.11. Conclusions: This study shows that a DEX-free OPF regimen is superior to the DEX-based OPD regimen and should be considered a standard option for CINV prophylaxis for HEC. Clinical trial information: CTRI/2022/03/040906 . [Table: see text]
Deep learning-based characterization of the drug tolerant persister cell state in lung cancer.
Authors: Wu Wei , Cech Lauren , Olivas Victor , Zaman Aubhishek , Kerr Daniel Lucas , Bivona Trever G. 
Published: August 2023

141 Background: Lung cancer is the leading cause of cancer-related lethality globally. Targeted therapies improve the clinical outcome of cancer treatment; however, a subpopulation of cancer cells survive during initial therapy and evolve into drug tolerant persister cells (DTPs) that maintain a residual disease reservoir. Residual disease preludes acquired resistance and tumor progression; therefore, identifying and eliminating DTPs could benefit future treatment paradigms. We have shown that Hippo pathway effector YAP1 (Yes Associated Protein-1) is activated in oncogene-driven lung cancers when cancer cells are exposed to various targeted therapies, such as EGFR, ALK, and RAS inhibitors. YAP1 transcriptional activation during targeted therapy is characterized by its increased nuclear localization and interaction with transcription factors. This activation promotes the expression of genes involved in cell survival, cellular plasticity, and metabolic reprogramming at the residual disease state. We hypothesized that detection of intrinsic or acquired persister cells may aid the development of optimized treatment regimens. In this study, we are building image-based deep learning models to identify YAP1 activation-mediated DTPs from histologically stained slides. Methods: H&E or YAP-stained immunohistochemistry (IHC) images from clinical lung cancer and patient-derived tumor xenograft samples were collected throughout targeted therapy and were annotated with semi-automation using high performance computing clusters. A deep learning model (U-Net algorithm) was used for image segmentation, training, validation, and testing. Results: 1638 images were annotated and over 80,000 patches from these images for YAP positive cells (or regions of interest) comprised the training dataset with semi-supervised automation. Subsequently, we built a customized deep-learning model to detect YAP-mediated DTP cell states from whole histopathological image slides. The deep learning-based model achieved excellent accuracy of 0.8238 and 0.9091 in training, and 0.8040 and 0.8949 in validation datasets for two different annotations, respectively. For a test dataset, the model obtained 0.81 and 0.902 accuracy for the two annotations, respectively. Conclusions: We have constructed a deep learning convolutional neural network model to infer the presence of the YAP1 activation-mediated drug tolerant persister cell state prior to or during targeted treatment of lung cancer. Implementing our AI-based model into routine lung cancer care in the future could identify patient subpopulations with YAP1 activated tumors who would most benefit from receiving YAP1-targeted small molecule inhibitors.
Efficacy and safety of pembrolizumab in advanced neuroendocrine tumors (NETs): A meta-analysis of clinical trials.
Authors: Chitkara Akshit , Patel Rushin , Anamika Fnu , Yang Chieh , Patel Zalak , Patel Femina , Patel Mrunal , Dhaliwal Jasninder , Ramaswamy Bhuvaneswari 
Published: August 2023

165 Background: Patients with recurrent or metastatic neuroendocrine tumors (NETs) have limited treatment options and a poor prognoses. Programmed death–ligand 1 (PD-L1) has been associated with the pathogenesis, progression, and prognosis of NETs. This article analyzes the efficacy and safety profile of PDL1 inhibitor Pembrolizumab in advanced NETs. Our objective is to identify all the clinical trials in which Pembrolizumab has been assessed in treating NETs and to evaluate the efficacy and safety profile in this patient population with high power. Methods: Systemic searches of PubMed, the Cochrane Library, and ClinicalTrials.gov were conducted with prespecified terms. The outcomes studied in our analysis were objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and serious adverse events (AEs). We calculated the pooled ORR for these studies using weighted pooled ORR = Σ (ORR_i * weight_i). We calculated the standard error using the formula SE = √(Σ (wi x (1 - ORRi) x ORRi) / Σ (wi x ni)) and the 95% confidence interval using the formula CI = ORR ± (1.96 x SE). This statistical method was also used to calculate our study's pooled DCR, PFS, OS, and serious AEs. However, it is essential to note that the pooled data is only as reliable as the individual data estimates from each study and may be subject to bias or confounding factors. Results: Five clinical trials met the inclusion criteria, including patients with gastrointestinal, pancreatic, lung, and lower genital tract NETs. Outcome data were available for all five studies, comprising 177 patients with NETs who received Pembrolizumab as part of the treatment regimen (Table). The pooled ORR and DCR were 5.27% (95% CI 2.23-8.31) and 5.4% (95% CI 3-7.8), respectively. The pooled weighted PFS was 4.4 months, and pooled weighted OS was 23.3 months. These findings suggest that Pembrolizumab has a promising ORR, DCR, PFS, and OS. Safety was a secondary endpoint, and the pooled serious AEs were noted in 20.9% of patients. Conclusions: Pembrolizumab demonstrated statistically significant efficacy and safety profile in a subset of patients with NETs and was overall well-tolerated. In recent years, the combination strategy of tyrosine kinase inhibitors (TKI) with PDL-1 checkpoint inhibitors has shown promising clinical benefits in patients with various solid tumors. Further studies are needed to analyze the efficacy and safety of TKI with PD-L1 inhibitors in NETs. [Table: see text]