ArticleNPJ precision oncology2021
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial.
Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.Nucleic acids research · 2026Article
- Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer.NPJ precision oncology · 2025Article
- The impact of targeted therapies on molecular alterations identified by an institutional molecular tumor board: an approach based on ESCAT classification.ESMO real world data and digital oncology · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Leveraging Large Language Models for Decision Support in Personalized Oncology.JAMA network open · 2023Article
- Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors.World journal of pediatrics : WJP · 2023Article
- Molecularly Stratified Treatment Options in Primary Refractory DLBCL/HGBL with MYC and BCL2 or BCL6 Rearrangements (HGBL, NOS with MYC/BCL6).Targeted oncology · 2023Article
- Consensus Statements on Precision Oncology in the China Greater Bay Area.JCO precision oncology · 2023Article
- Primary refractory plasmablastic lymphoma: A precision oncology approach.Frontiers in oncology · 2023Article
- In the literature: October 2021.ESMO open · 2021Article
- Personalized First-Line Treatment of Metastatic Pancreatic Neuroendocrine Carcinoma Facilitated by Liquid Biopsy and Computational Decision Support.Diagnostics (Basel, Switzerland) · 2021Article
- The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence.Cancer cell international · 2021Review
- A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial.NPJ precision oncology · 2021Article
- Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.
Identifiers
What OpenQuestion holds
Registered trials
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.