Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
The Use of Real World (RW) Data in Oncology
share announcement

The Use of Real World (RW) Data in Oncology

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: 31 August 2024 | Viewed by 16310

Special Issue Editor

Dr. Christopher T. Wilke

E-Mail Website
Guest Editor
Department of Radiation Oncology, University of Minnesota, Minneapolis, MN 55959, USA
Interests: radiation oncology; head and neck cancers; CNS tumors; functional imaging; tumor modeling; radiomics

Special Issue Information

Dear Colleagues,

Randomized clinical trials (RCTs) have long been considered the gold standard for rigorous evaluation of treatment efficacy for novel therapeutic interventions. In the field of oncology, specifically, RCTs form the backbone leading to the approval and adoption of nearly all new cancer-directed therapies. RCTs are not without their shortcomings, however, as they are frequently extremely time- and resource-demanding applications. RCTs have also come under increasing scrutiny as trial participants in these studies often do not reflect the breadth and diversity of the wider oncology patient population.

Recent technological advances in the electronic collection, storage, and dissemination of large amounts of medical data have enabled the analysis of sizeable cohorts of patients outside the confines of traditional clinical trials. These real-world (RW) data have many inherent advantages over traditional clinical trial-derived data in terms of cost, speed, and inclusion of large and diverse patient populations.

Major challenges nevertheless exist in the use of RW data. Missing or incomplete data and hidden biases can lead to incorrect conclusions regarding therapeutic interventions, and thus, techniques based upon RW data require thoughtful analysis and interpretation. This Special Issue will highlight both the challenges and the promises of incorporating RW data in modern clinical oncology practice.

Dr. Christopher T. Wilke
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • real-world data
  • big data
  • clinical trials
  • bioinformatics
  • health disparities
  • predictive analytics
  • radiomics
  • precision medicine
  • machine learning

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 3209 KiB  
Article
The Dutch CAR-T Tumorboard Experience: Population-Based Real-World Data on Patients with Relapsed or Refractory Large B-Cell Lymphoma Referred for CD19-Directed CAR T-Cell Therapy in The Netherlands
by Anne M. Spanjaart, Elise R. A. Pennings, Pim G. N. J. Mutsaers, Suzanne van Dorp, Margot Jak, Jaap A. van Doesum, Janneke W. de Boer, Anne G. H. Niezink, Milan Kos, Joost S. P. Vermaat, Aniko Sijs-Szabo, Marjolein W. M. van der Poel, Inger S. Nijhof, Maria T. Kuipers, Martine E. D. Chamuleau, Pieternella J. Lugtenburg, Jeanette K. Doorduijn, Yasmina I. M. Serroukh, Monique C. Minnema, Tom van Meerten and Marie José Kerstenadd Show full author list remove Hide full author list
Cancers 2023, 15(17), 4334; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15174334 - 30 Aug 2023
Cited by 2 | Viewed by 2184
Abstract
The real-world results of chimeric antigen receptor T-cell (CAR-T) therapy for patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) substantially differ across countries. In the Netherlands, the CAR-T tumorboard facilitates a unique nationwide infrastructure for referral, eligibility assessment and data collection. The aim [...] Read more.
The real-world results of chimeric antigen receptor T-cell (CAR-T) therapy for patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) substantially differ across countries. In the Netherlands, the CAR-T tumorboard facilitates a unique nationwide infrastructure for referral, eligibility assessment and data collection. The aim of this study was to evaluate real-world outcomes of axicabtagene ciloleucel (axi-cel) in the Dutch population, including the thus-far underreported effects on health-related quality of life (HR-QoL). All patients with R/R LBCL after ≥2 lines of systemic therapy referred for axi-cel treatment between May 2020–May 2022 were included (N = 250). Of the 160 apheresed patients, 145 patients received an axi-cel infusion. The main reason for ineligibility was rapidly progressive disease. The outcomes are better or at least comparable to other studies (best overall response rate: 84% (complete response: 66%); 12-month progression-free-survival rate and overall survival rate: 48% and 62%, respectively). The 12-month NRM was 5%, mainly caused by infections. Clinically meaningful improvement in several HR-QoL domains was observed from Month 9 onwards. Expert-directed patient selection can support effective and sustainable application of CAR-T treatment. Matched comparisons between cohorts will help to understand the differences in outcomes across countries and select best practices. Despite the favorable results, for a considerable proportion of patients with R/R LBCL there still is an unmet medical need. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

15 pages, 1639 KiB  
Article
Mapping the Oncological Basis Dataset to the Standardized Vocabularies of a Common Data Model: A Feasibility Study
by Jasmin Carus, Leona Trübe, Philip Szczepanski, Sylvia Nürnberg, Hanna Hees, Stefan Bartels, Alice Nennecke, Frank Ückert and Christopher Gundler
Cancers 2023, 15(16), 4059; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15164059 - 11 Aug 2023
Viewed by 1143
Abstract
In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) [...] Read more.
In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) operates as the legally required national standard for oncological reporting. Unfortunately, the usage of various documentation software solutions leads to semantic and technical heterogeneity of the data, complicating the establishment of research networks and collective data analysis. Within this feasibility study, we evaluated the transferability of all oBDS characteristics to the standardized vocabularies, a metadata repository of the observational medical outcomes partnership (OMOP) common data model (CDM). A total of 17,844 oBDS expressions were mapped automatically or manually to standardized concepts of the OMOP CDM. In a second step, we converted real patient data retrieved from the Hamburg Cancer Registry to the new terminologies. Given our pipeline, we transformed 1773.373 cancer-related data elements to the OMOP CDM. The mapping of the oBDS to the standardized vocabularies of the OMOP CDM promotes the semantic interoperability of oncological data in Germany. Moreover, it allows the participation in network studies of the observational health data sciences and informatics under the usage of federated analysis beyond the level of individual countries. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

12 pages, 396 KiB  
Article
Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence
by Dianne Bosch, Malou C. P. Kuppen, Metin Tascilar, Tineke J. Smilde, Peter F. A. Mulders, Carin A. Uyl-de Groot and Inge M. van Oort
Cancers 2023, 15(15), 3808; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15153808 - 27 Jul 2023
Viewed by 3242
Abstract
Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this [...] Read more.
Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. Methods: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. Results: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10–88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). Conclusions: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

13 pages, 1116 KiB  
Article
The Real-World Evidence on the Fragility and Its Impact on the Choice of Treatment Regimen in Newly Diagnosed Patients with Multiple Myeloma over 75 Years of Age
by Agata Tyczyńska, Marcela Krzysława Krzempek, Alexander Jorge Cortez, Artur Jurczyszyn, Katarzyna Godlewska, Hanna Ciepłuch, Edyta Subocz, Janusz Hałka, Anna Kulikowska de Nałęcz, Anna Wiśniewska, Alina Świderska, Anna Waszczuk-Gajda, Joanna Drozd-Sokołowska, Renata Guzicka-Kazimierczak, Kamil Wiśniewski, Agnieszka Porowska, Wanda Knopińska-Posłuszny, Janusz Kłoczko, Piotr Rzepecki, Dariusz Woszczyk, Hanna Symonowicz, Grzegorz Władysław Basak, Barbara Zdziarska, Krzysztof Jamroziak and Jan M. Zauchaadd Show full author list remove Hide full author list
Cancers 2023, 15(13), 3469; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15133469 - 02 Jul 2023
Viewed by 1220
Abstract
Fragility scales are intended to help in therapeutic decisions. Here, we asked if the fragility assessment in MM patients ≥ 75 years old qualified for treatment by the local physician correlates with the choice of treatment: a two- or three-drug regimens. Between 7/2018 [...] Read more.
Fragility scales are intended to help in therapeutic decisions. Here, we asked if the fragility assessment in MM patients ≥ 75 years old qualified for treatment by the local physician correlates with the choice of treatment: a two- or three-drug regimens. Between 7/2018 and 12/2019, we prospectively enrolled 197 MM patients at the start of treatment from the 13 Polish Myeloma Group centers. The data to assess fragility were prospectively collected, but centrally assessed fragility was not disclosed to the local center. The activity of daily living (ADL) could be assessed in 192 (97.5%) and was independent in 158 (80.2%), moderately impaired in 23 (11.7%), and 11 (5.6%) in completely dependent. Patients with more than three comorbidities made up 26.9% (53 patients). Thus, according to the Palumbo calculator, 43 patients were in the intermediate fitness group (21.8%), and the rest belonged to the frailty group (153, 77.7%). Overall, 79.7% of patients (157) received three-drug regimens and 20.3% (40) received two-drug regimens. In each ECOG group, more than three out of four patients received three-drug regimens. According to the ADL scale, 82.3% of the independent 65.2% of moderately impaired, and 81.8% of the dependent received three-drug regimens. Out of 53 patients with at least four comorbidities, 71.7% received three-drug regimens, and the rest received two-drug regimens. Thirty-four patients from the intermediate fit group (79.0%), and 123 (79.9%) from the frail group received three-drug regimens. Early mortality occurred in 25 patients (12.7%). No one discontinued treatment due to toxicity. To conclude, MM patients over 75 are mainly treated with triple-drug regimens, not only in reduced doses, regardless of their frailty scores. However, the absence of prospective fragility assessment did not negatively affect early mortality and the number of treatment discontinuations, which brings into question the clinical utility of current fragility scales in everyday practice. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

12 pages, 5246 KiB  
Article
A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology
by Talat Zehra, Sharjeel Anjum, Tahir Mahmood, Mahin Shams, Binish Arif Sultan, Zubair Ahmad, Najah Alsubaie and Shahzad Ahmed
Cancers 2022, 14(15), 3785; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14153785 - 03 Aug 2022
Cited by 6 | Viewed by 2770
Abstract
Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three [...] Read more.
Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

13 pages, 1434 KiB  
Article
Longitudinal Collection of Patient-Reported Outcomes and Activity Data during CAR-T Therapy: Feasibility, Acceptability, and Data Visualization
by Laura B. Oswald, Xiaoyin Li, Rodrigo Carvajal, Aasha I. Hoogland, Lisa M. Gudenkauf, Doris K. Hansen, Melissa Alsina, Frederick L. Locke, Yvelise Rodriguez, Nathaly Irizarry-Arroyo, Edmondo J. Robinson, Heather S. L. Jim, Brian D. Gonzalez and Kedar Kirtane
Cancers 2022, 14(11), 2742; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14112742 - 31 May 2022
Cited by 6 | Viewed by 2986
Abstract
Background: Clinicians must closely monitor patients for toxicities after chimeric antigen receptor T-cell therapy (CAR-T). Patient-reported outcomes (PROs) (e.g., toxicities, quality of life) and activity data (e.g., steps, sleep) may complement clinicians’ observations. This study tested the feasibility and acceptability of collecting PROs [...] Read more.
Background: Clinicians must closely monitor patients for toxicities after chimeric antigen receptor T-cell therapy (CAR-T). Patient-reported outcomes (PROs) (e.g., toxicities, quality of life) and activity data (e.g., steps, sleep) may complement clinicians’ observations. This study tested the feasibility and acceptability of collecting PROs and activity data from patients with hematologic malignancies during CAR-T and explored preliminary data patterns. Methods: Participants wore a Fitbit tracker and completed PROs at several timepoints through 90-days post-infusion. Feasibility was assessed with a priori benchmarks for recruitment (≥50%), retention (≥70%), PRO completion (≥70%), and days wearing the Fitbit (≥50%). Acceptability was assessed with participant satisfaction (a priori benchmark > 2 on a 0–4 scale). Results: Participants (N = 12) were M = 66 years old (SD = 7). Rates of recruitment (68%), retention (83%), PRO completion (85%), and days wearing the Fitbit (85%) indicated feasibility. Satisfaction with completing the PROs (M = 3.2, SD = 0.5) and wearing the Fitbit (M = 2.9, SD = 0.5) indicated acceptability. Preliminary data patterns suggested that participants with better treatment response (vs. progressive disease) had a higher toxicity burden. Conclusions: Longitudinal PRO and activity data collection was feasible and acceptable. Data collected on a larger scale may be used to specify risk prediction models to identify predictors of severe CAR-T-related toxicities and inform early interventions. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

Review

Jump to: Research

12 pages, 490 KiB  
Review
Leveraging Comprehensive Cancer Registry Data to Enable a Broad Range of Research, Audit and Patient Support Activities
by Belinda Lee, Lucy Gately, Sheau Wen Lok, Ben Tran, Margaret Lee, Rachel Wong, Ben Markman, Kate Dunn, Vanessa Wong, Matthew Loft, Azim Jalili, Angelyn Anton, Richard To, Miles Andrews and Peter Gibbs
Cancers 2022, 14(17), 4131; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14174131 - 26 Aug 2022
Cited by 7 | Viewed by 1804
Abstract
Traditional cancer registries have often been siloed efforts, established by single groups with limited objectives. There is the potential for registry data to support a broad range of research, audit and education initiatives. Here, we describe the establishment of a series of comprehensive [...] Read more.
Traditional cancer registries have often been siloed efforts, established by single groups with limited objectives. There is the potential for registry data to support a broad range of research, audit and education initiatives. Here, we describe the establishment of a series of comprehensive cancer registries across the spectrum of common solid cancers. The experience and learnings of each registry team as they develop, implement and then use collected data for a range of purposes, that informs the conduct and output of other registries in a virtuous cycle. Each registry is multi-site, multi-disciplinary and aims to collect data of maximal interest and value to a broad range of enquiry, which would be accessible to any researcher with a high-quality proposal. Lessons learnt include the need for careful and continuous curation of data fields, with regular database updates, and the need for a continued focus on data quality. The registry data as a standalone resource has supported numerous projects, but linkage with external datasets with patients in common has enhanced the audit and research potential. Multiple projects have linked registry data with matched tissue specimens to support prognostic and predictive biomarker studies, both validation and discovery. Registry-based biomarker trials have been successfully supported, generating novel and practice-changing data. Registry-based clinical trials, particularly randomised studies exploring the optimal use of available therapy options are now complementing the research conducted in traditional clinical trials. More recent projects supported by the registries include health economic studies, personalised patient education material, and increased consumer engagement, including consumer entered data. Full article
(This article belongs to the Special Issue The Use of Real World (RW) Data in Oncology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop