ArticleFrontiers in oncology2019
Staging System to Predict the Risk of Relapse in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation.
Article in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters.Journal of cellular and molecular medicine · 2023Article
- Prognostic nomogram for multiple myeloma early relapse after autologous stem cell transplant in the novel agent era.Cancer medicine · 2023Article
- Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys.Skeletal radiology · 2022Article
- A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial.Cancers · 2021Article
- AI-supported modified risk staging for multiple myeloma cancer useful in real-world scenario.Translational oncology · 2021Article
- Survival Outcomes of Newly Diagnosed Multiple Myeloma at a Tertiary Care Center in North India (IMAGe: 001A Study).JCO global oncology · 2021Article
- Evaluation of Prognostic Significance of the International Staging System According to Glomerular Filtration Rate in Newly Diagnosed Multiple Myeloma Patients Eligible for Autologous Stem Cell TransplantationTurkish journal of haematology : official journal of Turkish Society of Haematology · 2021Article
- MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma.Frontiers in oncology · 2021Article
- Artificial Intelligence in Hematology: Current Challenges and Opportunities.Current hematologic malignancy reports · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last decade autologous stem cell transplantation (ASCT) has emerged as the standard of care in the management of Multiple Myeloma (MM). However, the cases of early relapse (within 36 months) after the stem cell rescue remains a significant challenge. For a lot of practical purposes, it is crucial to identify whether a patient undergoing ASCT falls into the high-risk group (likely to relapse within 36 months) or a low risk one. Our analysis showed that existing MM staging systems (International Staging System or ISS and Durie Salmon Staging or DSS) are not sufficient to discriminate between the risk groups significantly. To address this, we gathered a total of 39 clinical and laboratory parameters of 347 patients from the Department of Medical Oncology of All India Institute of Medical Sciences (AIIMS). We employed a stacked machine learning model consisting spectral clustering and Fast and Frugal Tree (FFT) technique to come up with a 3-factor multivariate 2-stage staging scheme, which turns out to be extremely decisive about the outcome of the stem cell rescue. Our model comes up with a three-factor (1. if patients has relapsed following remission, 2. response to induction, 3. pre-transplant Glomerular Filtration Rate or GFR) staging scheme. The resulting model stratifies patients into high-risk and low-risk groups with markedly distinct progression-free (median survival-24 months vs. 91 months) and overall survival (median survival-51 months vs. 135 months) patterns.
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