Evidence map›Paper›PMID 33930310›Full record

ReviewCancer cell2021

Artificial intelligence for clinical oncology.

Benjamin H Kann, Ahmed Hosny, Hugo J W L Aerts

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer cell, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 207 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
207citing papers in PubMed, 6 pooled it
30.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

207 citing papers in PubMed, 6 syntheses or guidelines pooled it, 339 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Trial
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Article

147 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 2 countries.

Benjamin H KannArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Ahmed HosnyArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Hugo J W L AertsArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands. Electronic address: haerts@bwh.harvard.edu.
Brigham and Women's Hospital · US

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Unraveling the Complexities of Risk and Mechanism in CancerR35CA220523 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI QUACKENBUSH, JOHN · 2018 to 2024
$6.0M
Quantitative Radiomics System Decoding the Tumor PhenotypeU24CA194354 · NCI · DANA-FARBER CANCER INST · PI AERTS, HUGO, QUACKENBUSH, JOHN · 2015 to 2019
$3.5M
Genotype and Imaging Phenotype Biomarkers in Lung CancerU01CA190234 · NCI · DANA-FARBER CANCER INST · PI AERTS, HUGO, QUACKENBUSH, JOHN · 2015 to 2019
$3.3M
Development of an artificial intelligence-driven, imaging-based platform for pretreatment identification of extranodal extension in head and neck cancerK08DE030216 · NIDCR · BRIGHAM AND WOMEN'S HOSPITAL · PI KANN, BENJAMIN HARRIS · 2021 to 2025
$841k
NCI NIH HHS R35 CA220523NCI NIH HHS U01 CA190234NCI NIH HHS U01 CA209414NCI NIH HHS U24 CA194354NIDCR NIH HHS K08 DE030216
6 · The paper itself

Abstract

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.

Indexed as

AlgorithmsArtificial IntelligenceClinical Decision-MakingPrecision MedicineHumansMedical OncologyNeoplasmsartificial intelligencecare pathwayclinical oncologyclinical translationprecision medicine

Identifiers

PMID33930310
PMCPMC8282694
OpenAlexW3157202711

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.