Evidence map›Paper›PMID 29361815›Full record

ArticleCancer research and treatment2018

The NEAT Predictive Model for Survival in Patients with Advanced Cancer.

Amanda Zucker, Chiaojung Jillian Tsai, John Loscalzo, Pedro Calves, Johnny Kao

Registry-linked trialAbstract read
In one paragraph

Article in Cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06842030 (Feasibility Trial of Darwin OncoTreat and OncoTarget Precision Medicine Testing to Improve Outcomes for Patients With Limited Metastatic Disease That Failed First-Line Systemic Therapy), which is not on this map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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.

NCT06842030 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Feasibility Trial of Darwin OncoTreat and OncoTarget Precision Medicine Testing to Improve Outcomes for Patients With Limited Metastatic Disease That Failed First-Line Systemic Therapy

TypeinterventionalSponsorGood Samaritan Hospital Medical Center, New YorkRan2025 to 2027Enrolled20ConditionsPersonalized Medicine, Oligometastatic Disease, FeasibilityArmsDarwin OncoTarget and OncoTreat
3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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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

5 authors.

Amanda ZuckerDepartment of Radiation Oncology, Good Samaritan Hospital Medical Center, West Islip, NY, USA.
Chiaojung Jillian TsaiDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
John LoscalzoDivisions of Hematology and Medical Oncology, Good Samaritan Hospital Medical Center, West Islip, NY, USA.
Pedro CalvesDivisions of Supportive and Palliative Care, Good Samaritan Hospital Medical Center, West Islip, NY, USA.
Johnny KaoDepartment of Radiation Oncology, Good Samaritan Hospital Medical Center, West Islip, NY, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

purposeWe previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND

methodsFrom May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors ("N"), Eastern Cooperative Oncology Group performance status ("E"), albumin ("A") and primary tumor site ("T"). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance.

resultsNumber of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke's R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models.

conclusionThe NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer.

Indexed as

FemaleHumansLife ExpectancyLogistic ModelsMaleMultivariate AnalysisNeoplasmsNeoplasm StagingPrognosisLife expectancyPrognosisRadiation oncology

Identifiers

PMID29361815
PMCPMC6192914

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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.