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Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors
被引:2
|作者:
Vachani, Anil
[1
,2
]
Lam, Stephen
[8
]
Massion, Pierre P.
[3
]
Brown, James K.
[4
,5
]
Beggs, Michael
[6
]
Fish, Amanda L.
[6
]
Carbonell, Luis
[6
]
Wang, Shan X.
[6
]
Mazzone, Peter J.
[7
]
机构:
[1] Univ Penn, Dept Med, Pulm Allergy & Crit Care Div, Philadelphia, PA 19104 USA
[2] Corporal Michael J Crescenz VA Med Ctr, Dept Med, Philadelphia, PA 19104 USA
[3] Vanderbilt Univ, Div Allergy Pulm & Crit Care Med, Nashville, TN USA
[4] Univ Calif San Francisco, Div Pulm Crit Care Allergy & Sleep Med, Dept Med, San Francisco, CA USA
[5] VA Med Ctr San Francisco, Dept Med, San Francisco, CA USA
[6] Mag Array Inc, Milpitas, CA USA
[7] Cleveland Clin, Resp Inst, Cleveland, OH USA
[8] Univ British Columbia, Dept Integrat Oncol, British Columbia Canc Res Inst, Vancouver, BC, Canada
来源:
基金:
美国国家卫生研究院;
关键词:
biomarkers;
diagnosis;
lung cancer;
pulmonary nodules;
prediction;
LUNG-CANCER;
PROBABILITY;
MANAGEMENT;
MALIGNANCY;
ACCURACY;
PATIENT;
D O I:
10.1016/j.chest.2022.10.038
中图分类号:
R4 [临床医学];
学科分类号:
1002 ;
100602 ;
摘要:
BACKGROUND: Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis.RESEARCH QUESTION: What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model?STUDY DESIGN AND METHODS: Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model.RESULTS: In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, # 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratifi- cation by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher spec-ificity at the low-risk threshold and higher sensitivity at the very high-risk threshold.INTERPRETATION: The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.
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页码:966 / 976
页数:11
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