Abstract 1587P
Background
The treatment and survival associated with multiple cancers has dramatically improved over the past decade with therapeutic advances. While print and broadcast media often report on cancer, it is unknown how movies depict cancer, treatment, or outcomes. Given that film may influence and shape public opinion, and potential willingness to seek out cancer treatment, we evaluated the portrayal of cancer in movies.
Methods
We searched for movies released between 2010 and 2020 using IMDb.com with search references for cancer in keywords. We excluded non-English language movies to avoid error in translation. We viewed and coded movies to include character’s role, diagnosis, curability, treatment, race/ethnicity, and gender. Per institutional policies, the study was not submitted for IRB approval because all data are publicly available.
Results
We identified 104 movies depicting cancer and oncologists between 2010 and 2020. A total of 108 characters with cancer were portrayed. Female characters were depicted more often (n = 60, 55.6%) than male characters (n = 48, 44.4%). The type of cancer was most often not specified in the movie (n = 40, 37%). The majority of characters were portrayed as having an incurable cancer (n = 70, 64.8%), while the curability of cancer was unknown in 21.3% of roles (n = 23). Only 13.9% of characters (n = 15) were depicted as having a curable cancer. The cancer-directed therapy was not specified or depicted for most characters (n = 37, 34.3%). When treatment was depicted or specified, chemotherapy was the most common treatment shown (n = 36, 33.3%). Two characters were depicted as choosing alternative medicine. Twelve characters were shown to choose forgoing cancer directed treatment. Zero characters (0%) were shown receiving immune checkpoint inhibitors or genomic-based therapies. Hospice or palliative care was discussed and offered for 8 characters (7.4%).
Conclusions
Most movies released from 2010 to 2020 with an oncology storyline do not contain granular details about cancer type or treatment. Most characters were portrayed as having an incurable malignancy. Chemotherapy was the most commonly depicted therapy while newer therapies were not depicted. The portrayal of oncology in recent English-language movies does not accurately reflect the reality of cancer care.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
D.J. Benjamin: Financial Interests, Advisory Board: Astellas, Eisai, Seagen; Financial Interests, Speaker’s Bureau: Merck; Financial Interests, Other: Merck. A. Rezazadeh Kalebasty: Financial Interests, Personal, Advisory Board, advisory board and speaker: Exelixis, Bayer, Pfizer, Genentech, EMD Serono; Financial Interests, Personal, Advisory Board, speaker and advisory board: AstraZeneca, Novartis, BMS; Financial Interests, Personal, Invited Speaker: Janssen, Astellas Medivation, Pfizer, Novartis, Genentech/Roche, Eisai, AstraZeneca, BMS, Amgen, Exelixis, EMD Serono, Merck, Seattle Genetics/ Astellas, Myovant, Gilead Sciences; Financial Interests, Personal, Advisory Board: Gilead Sciences; Financial Interests, Institutional, Local PI: Genentech, Exelixis, Janssen, AstraZeneca, Bayer, BMS, Eisai, Macrogenics, Astellas, Beyond Spring, Bioclin Therapeutics, Clovis Oncology, Bavarian Nordic, Seattle Genetics, Immunomedics, Epizy, Arnivas, Navire, Point biopharma, Novartis. All other authors have declared no conflicts of interest.
Resources from the same session
1199P - Developing and systematically validating homologous recombination repair gene detection method based on next-generation sequencing
Presenter: Yi Sun
Session: Poster session 10
1200P - Investigation of multiphoton microscopy as an innovative tool for intraoperative section-free histologic investigations in just a few minutes
Presenter: Martí Homs Soler
Session: Poster session 10
1201P - Novel deep learning model and validation of whole slide images in lung cancer diagnosis
Presenter: Alhassan Ahmed
Session: Poster session 10
Resources:
Abstract
1202P - A deep learning approach using routine pathology images to guide precision medicine in metastatic CRC
Presenter: Chaitanya Parmar
Session: Poster session 10
1203P - Analytical evaluation of whole genome sequencing for acute myeloid leukemia
Presenter: Guidantonio Malagoli Tagliazucchi
Session: Poster session 10
1204P - Real-world utility of whole genome sequencing for patients with cancer: Evaluation of a regional implementation of the 100,000 genomes project
Presenter: Helen Robbins
Session: Poster session 10
1205P - A retrospective machine learning-based analysis of nationwide cancer CGP data across cancer types to identify features associated with recommendation of mutation-based therapy
Presenter: Hiroaki Ikushima
Session: Poster session 10
1478P - Dual single-nucleotide polymorphism biomarker combination to select opioid for cancer pain management
Presenter: Yoshihiko Fujita
Session: Poster session 10
1479P - Use of rescue opioids and pain control after ketamine initiation in refractory cancer pain: A multicentric observational study
Presenter: Pablo Gallardo Melo
Session: Poster session 10
1480P - Long term therapy with denosumab and zoledronic acid: A comparative real-world retrospective observational study on skeletal-related events and pain in patients with metastatic breast cancer
Presenter: Giacomo Massa
Session: Poster session 10