ArticleVeterinary sciences2021
Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model.
Article in Veterinary sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 10 citations in OpenAlex.
- Exploratory Evaluation of Hydrogen-Rich Water Therapy for Keloid Management: A Double-Blinded Randomized Pilot Trial.Aesthetic plastic surgery · 2026Trial
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Identification of novel genetic mutations for the treatment prognostication of canine lymphoma.NPJ precision oncology · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research.Frontiers in veterinary science · 2024Review
- Multimodal machine learning models identify chemotherapy drugs with prospective clinical efficacy in dogs with relapsed B-cell lymphoma.Frontiers in oncology · 2024Article
- Monitoring plasma nucleosome concentrations to measure disease response and progression in dogs with hematopoietic malignancies.PloS one · 2023Article
- Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study.Frontiers in veterinary science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
First-line treatments of cancer do not always work, and even when they do, they cure the disease at unequal rates mostly owing to biological and clinical heterogeneity across patients. Accurate prediction of clinical outcome and survival following the treatment can support and expedite the process of comparing alternative treatments. We describe the methodology to dynamically determine remission probabilities for individual patients, as well as their prospects of progression-free survival (PFS). The proposed methodology utilizes the ex vivo drug sensitivity of cancer cells, their immunophenotyping results, and patient information, such as age and breed, in training machine learning (ML) models, as well as the Cox hazards model to predict the probability of clinical remission (CR) or relapse across time for a given patient. We applied the methodology using the three types of data obtained from 242 canine lymphoma patients treated by (L)-CHOP chemotherapy. The results demonstrate substantial enhancement in the predictive accuracy of the ML models by utilizing features from all the three types of data. They also highlight superior performance and utility in predicting survival compared to the conventional stratification method. We believe that the proposed methodology can contribute to improving and personalizing the care of cancer patients.
Indexed as
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.