Evidence map›Paper›PMID 38872212›Full record

ReviewJournal of translational medicine2024

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.

Alireza Baniasadi, Jeeban P Das, Conor M Prendergast, Zahra Beizavi, Hong Y Ma, Muhammad Yaman Jaber, Kathleen M Capaccione

Abstract readReview
In one paragraph

Review in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. The Development of AlPharmaceutics · 2025
    Article
  6. Review
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

7 authors.

Alireza BaniasadiDepartment of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA. Alireza.baniasadi.md@gmail.com.ORCID 0000-0002-3314-6052
Jeeban P DasDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Conor M PrendergastDepartment of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
Zahra BeizaviDepartment of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
Hong Y MaDepartment of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
Muhammad Yaman JaberDepartment of Radiology, Damascus University, Damascus, Syria.
Kathleen M CapaccioneDepartment of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

Indexed as

Diagnostic ImagingFibrosisNeoplasmsAnimalsHumansCancerDiagnosisFibrosisImaging techniquesTumor microenvironment

Identifiers

PMID38872212
PMCPMC11177383

What OpenQuestion holds

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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.