bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evidence supporting innovative cancer care delivery model for overcoming health disparity.
Authors: Pendharkar Dinesh , Tripathi Chandramauli , Srivastav Kalpana , Kumar Uttam , Nagar Ashok Kumar 
Published: June 2023

e13521 Background: Disparity in cancer care is the biggest challenge faced by the health systems. The solution could be brought out by new models of service delivery. To mitigate the challenge, an innovative district cancer care model was launched across 6 states of India. A cancer care unit has been established in government district hospitals, which constitute pivot of health systems in all low- and middle-income countries of the world . It assists cancer patients in complete care continuum from diagnosis to end of life care. Here, we report how a district cancer care model (Pendharkar model) proved its validity during the covid pandemic, by providing relentless cancer care, when specialized cancer services were paralyzed in the country. Methods: Patient data were collected from four government district hospitals of Ujjain (Madhya Pradesh) Bhilwara, Jhalawar and Churu (Rajasthan), over three years, including period of Covid pandemic and lockdown. The numbers were analyzed for attendance in outpatient and inpatient services, numbers of chemotherapy sessions administered, from January 2019 to December 2021. Standard statistical calculations were done. Results: A total of 4873 new patients were registered in the cancer units (1614,1678,1581 per year), over the period of three years: 2019, 2020, 2021. Annually, there were 695,643 and 609, new patients registered in the Ujjain district, 190,252 and 204 in Churu, 349. 276.and 276 in Jhalawar, and 380,507 and 442 in Bhilwara. Over a three-year period, there were 37183 outpatient visits in total (11416, 13665, and 12102 in 2019, 2020, and 2021 respectively). The number of inpatient admissions were 18593 (5047,7077,6469 respectively). A total of 17639 sessions of chemotherapy were performed during this period (5133,6361,8745 in 2019, 2020, and 2021 respectively). The data showed no change despite pandemic lockdown. Conclusions: Utilization of outpatient and inpatient services, as well procedures rendered, did not change despite covid and lockdown in caner units of government district hospitals. The data clearly demonstrates the robustness and resilience of this health care model of system strengthening, using alternate oncology workforce, task sharing and capacity building. It worked equally efficiently in different parts of India and maintained its effectiveness even during pandemic, when complete collapse of healthcare was noticed around the world. The oncology societies and international organizations could take more interest in analyzing and improving this delivery model, which has immense capability of reducing health disparities.[Table: see text]
Time to initial review by oncology clinical pathways committees for new FDA approvals/label expansions.
Authors: Caranfa Christine L. , Addonizio Joanna , Desravines Belinda , Rezaee Daniel , Waid Omar , Hamilton Joanna M. , Selesnick Victoria H. , Scalzo Michael , Jackman David Michael 
Published: June 2023

e13523 Background: Dana-Farber Cancer Institute’s (DFCI) clinical pathways provide expert oncology decision-support. We schedule 2-4 reviews/year for each of over 40 medical oncology pathways, plus ad hoc reviews. When FDA issues new approvals or label expansions, the relevant medical director assigns it to the next scheduled review or a more urgent ad hoc meeting. Here we explore the time to initial review (TTIR) for FDA approvals. Methods: This analysis includes FDA approvals/label expansions for adult oncology patients from 01/01/2021 – 12/31/2022. We excluded CAR-T therapies as well as FDA approvals not directed at one of our existing pathways. TTIR was calculated from date of FDA approval to date of DFCI clinical pathways review. Univariate analyses examined the impact of prior in-class competitors in the specific approval setting; prior approvals for the treatment class in general; and scheduled meeting cadence. Results: From January 1, 2021 – December 31, 2022, there were 58 FDA approvals/label expansions in diseases for which a DFCI pathway exists. Eighteen represented items that had been previously addressed (e.g., conversion of accelerated approval to full, or a label expansion adopted into pathways earlier based on published data). For the remaining 40 new approvals or label expansions, the median TTIR was 3.4 months. 43% of FDA approvals were addressed prior to or within 30 days after announcement. There were 9 ad hoc reviews over this period, including review of 3 new FDA approvals. Median TTIR for items that were a first-in-class therapy for that specific indication (n= 20) was 2.1 months, compared to 3.5 months for agents with an existing in-class competitor in that setting (n=20). Median TTIR for items that represented the first approval for a novel first-in-class agent (n=13) was 1.5 months; TTIR for agents with existing in-class approvals in any setting (n=27) was 3.5 months. Median TTIR for FDA actions involving diseases/pathways that have > 3 scheduled review meetings/yr (n=28) was 2.8 months, compared to 5.2 months in pathways with 1-2 scheduled reviews/yr (n=12). Conclusions: Clinical Pathways provide a structured mechanism for reviewing whether, when, and how new treatments are best used in oncology practice, and for disseminating those recommendations to providers. The time to initial review was shorter for initial registrational approvals for novel agents, and for treatments in diseases with more frequent meetings. [Table: see text]
A comparison between a clinical decision support system and clinicians with guidelines in breast cancer.
Authors: Li Jianbin , Yuan Yang , Bian Li , Yang Hua , Ma Li , Xin Ling , Li Feng , Zhang Shaohua , Wang Tao , Liu Yinhua , Jiang Zefei 
Published: June 2023

e13551 Background: We are building a clinical decision support system (CSCO AI) for breast cancer patients to improve the efficiency of clinical decision-making. We aimed to assess cancer treatment regimens, in neoadjuvant therapy, adjuvant chemotherapy, adjuvant endocrine therapy, first line therapy and second line therapy, given by CSCO AI and clinicians. Methods: 400 breast cancer patients were screened from the CSCO database. Clinicians with similar levels were randomly assigned one of the volumes (200 cases). After that, clinicians with guidelines were asked to answer the same cases again. CSCO AI was asked to assess all cases. Three reviewers were independently asked to evaluate the regimens from clinicians and CSCO AI. Regimens were masked before evaluation. The primary outcome was the proportion of high-level conformity (HLC), which were defined as the proportions of regimens in accordance with CSCO guidelines. Results: The overall concordance between clinicians and CSCO AI was 67.4% (2350/3500). After referring to the guideline, a total of 22.6% (792/3500) regimens were modified by clinicians, 12.9% (451/3500) had a higher grades and 9.7% (341/3500) had a lower grades. In early stage, the concordance was elevated with statistical significance from 71.3% (1497/2100) to 76.1% (1598/2100, p＜0.001). In the metastatic stage, the concordance was improved form 61.7% (864/1400) to 66.0% (924/1400, p=0.018). HLC in CSCO AI was 95.8% (95%CI:94.0%-97.6%), significantly higher than that in clinicians (90.8%, 95%CI:89.8%-91.8%) and in clinicians with guidelines (92.1%, 95%CI:91.0%-93.4%). In early stage, high-level conformity in CSCO AI was 95.7%, with no statistical significance when compared with clinicians (92.7%, p=0.078) and clinicians with guidelines (92.3%, p=0.050). In metastatic stage, high-level conformity in clinicians was only 88.0%, lower than that in CSCO AI (96.0%, p=0.001). However, after referring guidelines, high-level conformity in clinicians was elevated to 91.9%, with no significant difference when compared with that in CSCO AI (p=0.058). Considering professions, the high level conformity of surgeons was 85.9%, lower than that of CSCO AI (OR=0.25,95%CI: 0.16-0.41). The most significant difference in HLC was in first-line therapy (OR=0.06, 95%CI:0.01-0.41). When clinicians were divided according to their levels, there was no statistical significance between CSCO AI and higher-level clinicians. Conclusions: Clinical decision support for breast cancer was superior for most process outcomes except for second-line therapy. The improvements in process outcomes suggest that CSCO AI can be widely used in clinical practice.
Novel machine learning algorithm to predict response to immunotherapy in patients with small cell and non-small cell lung cancer.
Authors: Sharma Lakshya , Choi Sunyoung , Balaji Vaishnavi , Joshi Adwait , Mporas Iosif , Shi Jiaqi , Zhao Yi , Tailor Vipula , Shah Riyaz N.H. , Waters Justin S. , Adeleke Sola Michael 
Published: June 2023

e13556 Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]
Cost and time estimates for integrating a cardiovascular health application for cancer survivors in the electronic health record: Case studies from the NCORP network.
Authors: Foraker Randi E. , Durkin Michael , Lackey Ian , Heard Kevin , Wells Brian J. , Fesperman Susan , Davidson Eleanor , Payne Philip , Lai Albert M. , Gendron Melissa , Weaver Kathryn E. 
Published: June 2023

e13557 Background: Clinical decision support (CDS) tools may support the delivery of guideline concordant cancer care yet must be integrated into EHR platforms and optimized for clinical workflow. Implementation outcomes are not well-documented in the literature for deployment of CDS tools in community oncology practices. Here we provide best practices for teams looking to implement CDS tools in diverse cancer care settings. Methods: The Automated Heart-Health Assessment (AH-HA-WF-1804CD) study is a clinic-randomized trial coordinated by the Wake Forest NCI Community Oncology Research Program (NCORP) Research Base to support cardiovascular health assessment among cancer survivors. The AH-HA team developed and guided the implementation of the AH-HA tool, an EHR-based CDS visualization of the American Heart Association's Life's Simple 7 modifiable risk factors supplemented with cancer treatment information. Four NCORP clinics were randomized to the intervention arm and implemented the tool within their EHR leveraging Fast Healthcare Interoperable Resources (FHIR) standards and best practice advisories (BPAs). Information technology (IT) team authorization was required prior to randomization. We provided an implementation checklist to each clinic alongside virtual support from experts in implementing CDS for research and operational purposes. We describe our successful CDS tool implementation strategy and report the timeline for implementation at each clinic. Results: Although all clinics utilized the same EHR platform (Epic), clinics differed in terms of their time to approval, implementation, and tool activation, ranging in a total from 4 to 15 months. Although we provided step-by-step guidance for CDS deployment, each site required at least one technical assistance consultation. The estimated cost at each implementation site to deploy the tool, given a range of 40 to 80 hours at $150/hour, was $6,000 to $12,000 US dollars. Conclusions: Strengths of the implementation process included extensive documentation and clear communication of the steps for implementation. Clinics desired flexibility to adapt BPAs to their workflows. Site-level differences may pose challenges for interpretation of study implementation outcomes, including patient-level reach measures. Studies testing the impact of similar CDS tools should plan for clinic-level adaption and consider variability in timelines and assistance needed within the overall study timeline and budget. Clinical trial information: NCT03935282 .
Morphomic phenotyping of oncologic populations.
Authors: Wang Stewart , Wang Nicholas C. 
Published: June 2023

e13572 Background: We have previously identified body and organ composition measures in pre-treatment CT scans that predict post-treatment mortality or disease recurrence in melanoma as well as esophageal, pancreatic, hepatocellular and lung cancers within single institution cohorts in the US. Similar trends can be identified in Asian cohorts although the absolute range of these body composition measures differ substantially from those in the US. We have also identified body and organ composition measures that predict drug tissue distribution and optimal dosing. The qualitative and relative nature of body and organ composition measures to date have limited their utility for precision treatment of oncologic populations. We have therefore refined the precision, granularity and reliability of these measures (morphomics) in oncologic and reference populations. Methods: Analytic Morphomics, our group’s imaging analysis platform, extracts granular, anatomically-indexed body and organ composition measures from medical imaging. This platform has been updated to use artificial intelligence and nested, supervised machine learning algorithms. Our digital morphomics “assays” are 100% reproducible due to our extensive use of deterministic software algorithms and scalable due to our use of efficient ML algorithms and software code. Our ML models have been trained using state-of-the art techniques in 2D and 3D convolutional neural networks (CNNs) for image segmentation, utilizing repeated randomization cross-validation, hold-out sets, and ensemble modeling methods, with large, broadly representable training image sets, and verified by extensive user testing on clinical scans from nearly 200,000 live subjects. Results: We analyzed curated cohorts reflecting the general United States and Taiwanese population (Ns=5,286; 1,459). In addition, we analyzed oncological cohorts reflecting patients with colon (N=95), ovarian (216), pancreatic (156), and esophageal cancers (Ns=333, 344). Validated morphomic measures include bone mineral density (BMD), visceral fat area (VAT, subcutaneous fat area (SAT), skeletal muscle area (SMA), skeletal muscle density (MD) at multiple vertebral levels. When referenced against measures taken from general non-oncologic (trauma) populations to adjust for non-specific changes due to age and gender, these referenced morphomic measures gain predictive power for mortality and cancer recurrence. Conclusions: The unprecedented level of granularity and precision of these raw and referenced morphomic measures shows clinical utility for improved patient selection, drug dosing as well as evaluation of treatment response and toxicity.
Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model.
Authors: Nye Logan , Ghaednia Hamid , Schwab Joseph H. 
Published: June 2023

e13592 Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes problematic for rare diseases, where imaging datasets of such size do not exist. This makes it impossible to train digital pathology models for these rare conditions. However, recent advances in generative deep learning models may provide a method for overcoming this lack of histology data for rare diseases. Pre-trained diffusion-based probabilistic models can be used to create photorealistic variations of existing images. In this study, we explored the potential of using a deep generative model created by OpenAI for the purpose of producing synthetic histopathology images, using chondrosarcoma as our rare tumor of interest. Methods: Our team compiled a dataset of 55 chondrosarcoma histolopathology images from the annotated records of Dr. Henry Jaffe, a pioneering authority in musculoskeletal pathology. We built a deep learning image-generation application in a Jupyter notebook environment, iterating upon OpenAI’s DALL-E application processing interface (API) with python programming language. Using the chondrosarcoma histology dataset and NVIDIA GPUs, we trained the deep learning application to generate multiple synthetic variations of each real chondrosarcoma image. Results: After several hours, the deep learning model successfully generated 1,000 images of chondrosarcoma from 55 original images. The synthetic histology images retained photorealistic quality and displayed characteristic cellular features of chondrosarcoma tumor tissue. Conclusions: Deep generative models may be useful in addressing issues of data scarcity in rare diseases, such as chondrosarcoma. For example, in situations where existing imaging data is insufficient for training diagnostic computer vision models, diffusion-based generative models could be applied to create training datasets. However, further exploration of ethical considerations and qualitative analyses of these generated data are needed.
Standardizing the strategy in glioblastoma trials to promote success in cell and gene therapy clinical development.
Authors: Elorza Izaskun , Nirenberg Damian , Maczkiewicz Martin , Hoppener Floris 
Published: June 2023

e13604 Background: Glioblastoma is the most common and aggressive brain tumor, with a high recurrence rate and low survival. We are increasing our understanding of its biology, and more targeted therapeutical agents are available. However, the GBM prognosis has not improved over the last decades. Multiple clinical trials on Cell and Gene Therapies are ongoing but, there is a huge need to define standardized and innovative strategies to promote better patient selection, implement adaptative designs and specific laboratory assays, and tailor the clinical endpoint assessments. Methods: Medical experts panel agreed on the following insights and gave advice to improve the design and conduct of GBM trials. Results: The criteria for selecting patients are not uniform among studies. The 2021 WHO Classification of central nervous system tumors has yet to be universally adopted, and different tumor entities are being treated as GBM according to the prior 2016 classification. Furthermore, the required genetic testing panel is only available in some countries as part of routine testing. A wide range of CGT therapies are being studied with different mechanisms of action and are well suited to be administered as combinatory regimens and multiple-dosing schedules to boost the antitumor effect. The implementation of master protocols offers the possibility to test different schedules to identify the most promising for further investigation during early phase development. There is a lack of biomarkers for the patient- or cancer-specific sensitivity and the efficacy of CGT. This is important not only to target clinical treatments to the appropriate patients and identify responding/non-responding patients early during treatment but also to improve the preclinical models and identify targets for further therapeutic development. Investigators should use standard endpoint criteria when reporting clinical outcomes. It has been common to refrain from introducing any preliminary-effect objective in the early stages. This tendency changes after guidance from experts and regulatory agencies about maximizing early-phase data. Another vital endpoint in the early phases is assessing patient immunogenicity against CG therapy based on humoral and cell virus-specific and tumor-specific immune responses. This is especially important as there are significant concerns in translating preclinical safety results to the clinic due to the species-specificity of different viruses and critical differences between immune systems. Conclusions: Today, the purpose of treatment for GBM is to extend the survival period and to manage the disease while preserving the patient’s quality of life. The goal of CGT is a durable complete response, but to achieve this target, we will need a deep analysis of how we perform clinical trials and move to an expert and tailored research environment.