Improving Cancer Diagnosis and Care: Patient Access to Oncologic Imaging Expertise

Nass, Sharyl J.; Cogle, Christopher R.; Brink, James A.; Langlotz, Curtis P.; Balogh, Erin; Muellner, Ada; Siegal, Dana; Schilsky, Richard L.; Hricak, Hedvig

doi:10.1200/jco.18.01970

Cited by 12 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, as has already been noted, there also is a need to expand patient access to cancer imaging expertise [ 1 ], and the study also highlights that the work of dedicated cancer radiologists is greatly appreciated and needed. The added value of a radiologist subspecialized by organ or system is well known and highly valued.…”

Section: Discussionmentioning

confidence: 72%

“…However, most of the interviewed oncologists recognized the added value of a radiologist with a subspecialty in cancer imaging. Since subspecialization in radiology is currently generally categorized by organs or systems, and cancer imaging is not a formally recognized subspecialty in most countries, it is worth asking whether this method of training ensures intensive training and sufficient expertise in cancer imaging [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

“…An article published in the Journal of Clinical Oncology in 2019 raised awareness of the failure to deliver consistent, high-quality oncologic imaging services for most radiology practice models in the United States [ 1 ]. Moreover, according to a review conducted in 2018 by Schlemmer et al [ 2 ], numerous studies have shown substantial rates of disagreement (13–56%) between initial cancer imaging reports and second-opinion readings by radiologists specializing in cancer imaging, with second-opinion reports indicating the need for change in patient management in between 13 and 53.5% of cases [ 3 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…To improve patient access to oncologic imaging expertise, Nass et al [ 1 ] highlight the need for leaders in the radiology and health care communities to find solutions designed to enhance expertise in cancer imaging, as subspecialization in radiology centres is currently specialized by organ or system, and cancer imaging is not formally recognized as a subspecialty in most countries, which may lead to a lack of intensive training or expertise in oncologic imaging.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Radiology reporting in oncology—oncologists’ perspective

2021

View full text Add to dashboard Cite

Background Structured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists’ opinions and expectations concerning the radiologist’s report to identify general needs in daily practice and ways to improve interdisciplinary communication. Methods A 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation. Results A total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1–3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists’ service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists’ service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist. Conclusion Structured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.

show abstract

Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radiology reporting in oncology—oncologists’ perspective

2021

View full text Add to dashboard Cite

show abstract

“…From the oncology clinician perspective, interpreting multi-biomarker tests results, such as those from next-generation sequencing (NGS), is challenging because of a lack of time and lack of expertise. Discussions from two recent National Academy of Medicine workshops identified the need for oncologists, pathologists, radiologists, bioinformatic engineers, geneticists, and molecular biologists to work more closely together in a multi-disciplinary team [ 6 , 7 ]. Workshop participants recognized that tumor boards or precision oncology boards are a natural venue for this articulation of disciplines.…”

Section: Introductionmentioning

confidence: 99%

Building a precision oncology workforce by multidisciplinary and case-based learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Participants in two recent National Academy of Medicine workshops identified a need for more multi-disciplinary professionals on teams to assist oncology clinicians in precision oncology. Methods We developed a graduate school course to prepare biomedical students and pharmacy students to work within a multidisciplinary team of oncology clinicians, pathologists, radiologists, clinical pharmacists, and genetic counselors. Students learned precision oncology skills via case-based learning, hands-on data analyses, and presentations to peers. After the course, a focus group session was conducted to gain an in-depth student perspective on their interprofessional training experience, achievement of the course learning outcomes, ways to improve the course design in future offerings, and how the course could improve future career outcomes. A convenience sampling strategy was used for recruitment into the focus group session. A thematic content analysis was then conducted using the constant comparative method. Results Major themes arising from student feedback were (1) appreciation of a customized patient case-based teaching approach, (2) more emphasis on using data analysis tools, (3) valuing interdisciplinary inclusion, and (4) request for more student discussion with advanced preparation materials. Conclusions Feedback was generally positive and supports the continuation and expansion of the precision oncology course to include more hands-on instruction on the use of clinical bioinformatic tools.

show abstract

Clinical Application of Computational Methods in Precision Oncology

et al. 2020

Self Cite

View full text Add to dashboard Cite

IMPORTANCEThere is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems.OBJECTIVE To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. EVIDENCE REVIEWThe National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy;(2) computational methods for analysis, interpretation, and use of oncology data; and(3) clinical infrastructure and expertise for best use of computational precision oncology.FINDINGS Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics.CONCLUSIONS AND RELEVANCE Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

show abstract

Improving Cancer Diagnosis and Care: Patient Access to Oncologic Imaging Expertise

Cited by 12 publications

References 25 publications

Radiology reporting in oncology—oncologists’ perspective

Radiology reporting in oncology—oncologists’ perspective

Building a precision oncology workforce by multidisciplinary and case-based learning

Clinical Application of Computational Methods in Precision Oncology

Contact Info

Product

Resources

About