ReviewMedicina (Kaunas, Lithuania)2025
Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.
Review in Medicina (Kaunas, Lithuania), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.PLOS digital health · 2026Article
- A deep learning-based radiomics model for noninvasive diagnosis of melanoma.Melanoma research · 2026Article
- A Clinical-Radiomics Nomogram Based on Pretreatment Magnetic Resonance Imaging Predicting Tumor Residual at the End of Radiotherapy in Patients With Nasopharyngeal Carcinoma.International journal of biomedical imaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Nasopharyngeal carcinoma (NPC) represents a prevalent malignant tumor within the head and neck region, and enhancing the precision of prognostic assessments is a critical objective. Recent advancements in the integration of artificial intelligence (AI) and medical imaging have spurred a surge in research focusing on NPC image analysis through AI applications, particularly employing radiomics and artificial neural network approaches. This review provides a detailed examination of the prognostic advancement in NPC, utilizing imaging studies based on radiomics and deep learning techniques. The findings from these studies offer a promising outlook for achieving exceptionally precise prognoses regarding survival and treatment responses in NPC. The limitations of existing research and the potential for further application of radiomics and deep learning in NPC imaging are explored. It is recommended that future research efforts should aim to develop a comprehensive, labeled dataset of NPC images and prioritize studies that leverage AI for NPC screening.
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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.