Evidence map›Paper›PMID 36413412›Full record

ArticleCancer2023

Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma.

Bharat Nandakumar, Francis Baffour, Nadine H Abdallah, Shaji K Kumar, Angela Dispenzieri, Francis K Buadi, David Dingli, Martha Q Lacy, Suzanne R Hayman, Prashant Kapoor and 14 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
3.3field-weighted citation impact, top 6% of its field
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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.

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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

24 authors at 1 institution in 1 country.

Bharat NandakumarDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0002-5399-9116
Francis BaffourDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Nadine H AbdallahDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0001-9195-1589
Shaji K KumarDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0001-5392-9284
Angela DispenzieriDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Francis K BuadiDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
David DingliDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Martha Q LacyDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Suzanne R HaymanDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Prashant KapoorDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Nelson LeungDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Amie FonderDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Miriam HobbsDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Yi Lisa HwaDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Eli MuchtarDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Rahma WarsameDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Taxiarchis V KourelisDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Ronald S GoDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Robert A KyleDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Morie A GertzDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
S Vincent RajkumarDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Jason KlugDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Panagiotis KorfiatisDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Wilson I GonsalvesDivision of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
Mayo Clinic · US

Funding

Project 4: Targeting Resistance to T-Cell Directed Therapy in Multiple MyelomaP50CA186781 · NCI · MAYO CLINIC ARIZONA · PI Rafael Fonseca · 2015 to 2026
$25.5M
Assessment of Metabolites Among Asymptomatic Precursor and Malignant Monoclonal GammopathiesR01CA254961 · NCI · MAYO CLINIC ROCHESTER · PI GONSALVES, WILSON · 2021 to 2024
$1.5M
NCI NIH HHS P50 CA186781NCI NIH HHS R01 CA254961
6 · The paper itself

Abstract

backgroundSarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.

methodsSarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not.

resultsThe study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri-diagnosis standard-dose CT scan. The median overall survival (OS) and 2-year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics.

conclusionsSarcopenia identified by a machine learning-based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning-based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

Indexed as

Deep LearningMultiple MyelomaSarcopeniaHumansIn Situ Hybridization, FluorescenceMuscle, SkeletalPrognosisProspective StudiesRetrospective StudiesTomography, X-Ray Computedartificial intelligencemultiple myelomaprognostic factors in multiple myelomasarcopeniasurvival outcomes in multiple myeloma

Identifiers

PMID36413412
PMCPMC9822865
OpenAlexW4309667928

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.