Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Article Swipe

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Kezhong Chen , Yuntao Nie , Samina Park , Kai Zhang , Yangming Zhang , Yuan Liu , Bengang Hui , Lixin Zhou , Xun Wang , Qingyi Qi , Hao Li , Guannan Kang , Yuqing Huang , Yingtai Chen , Jia‐Bao Liu , Jian Cui , Mingru Li , In Kyu Park , Chang Hyun Kang , Haifeng Shen , Yingshun Yang , Tian Guan , Yaxiao Zhang , Fan Yang , Young Tae Kim , Jun Wang ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1158/1078-0432.c.6530948.v1

Purpose:Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making.Experimental Design:A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort.Results:The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854–0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859–0.916), higher than those of the Brock model [0.806 (0.771–0.838)], PKU model [0.780 (0.743–0.817)], Mayo model [0.739 (0.697–0.776)], and VA model [0.682 (0.640–0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815–0.915)] was higher than that of surgeons [0.790 (0.711–0.852), 0.741 (0.662–0.804), and 0.727 (0.650–0.788)], radiologist [0.748 (0.671–0.814)], and the CADx system [0.757 (0.682–0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity.Conclusions:After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

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Medicine Confidence interval Malignancy Cohort Prospective cohort study Nodule (geology) Internal medicine Machine learning Computer science Biology Paleontology

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1158/1078-0432.c.6530948.v1
OA Status: gold
References: 33
Related Works: 10
OpenAlex ID: https://openalex.org/W4361953908

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DOI: https://doi.org/10.1158/1078-0432.c.6530948.v1

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Title: Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-03-31

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Authors: Kezhong Chen, Yuntao Nie, Samina Park, Kai Zhang, Yangming Zhang, Yuan Liu, Bengang Hui, Lixin Zhou, Xun Wang, Qingyi Qi, Hao Li, Guannan Kang, Yuqing Huang, Yingtai Chen, Jia‐Bao Liu, Jian Cui, Mingru Li, In Kyu Park, Chang Hyun Kang, Haifeng Shen, Yingshun Yang, Tian Guan, Yaxiao Zhang, Fan Yang, Young Tae Kim, Jun Wang

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Landing page: https://doi.org/10.1158/1078-0432.c.6530948.v1

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Concepts: Medicine, Confidence interval, Malignancy, Cohort, Prospective cohort study, Nodule (geology), Internal medicine, Machine learning, Computer science, Biology, Paleontology

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referenced_works	https://openalex.org/W2080448710, https://openalex.org/W2946185430, https://openalex.org/W2114175038, https://openalex.org/W2150780222, https://openalex.org/W2771553096, https://openalex.org/W2005098328, https://openalex.org/W2111547563, https://openalex.org/W2934399013, https://openalex.org/W2295598076, https://openalex.org/W2049674541, https://openalex.org/W2982942149, https://openalex.org/W1988198826, https://openalex.org/W2074040754, https://openalex.org/W2322371438, https://openalex.org/W581215525, https://openalex.org/W2190649833, https://openalex.org/W2947167326, https://openalex.org/W3010442991, https://openalex.org/W2114374668, https://openalex.org/W2962862931, https://openalex.org/W1807492271, https://openalex.org/W3098897816, https://openalex.org/W1938255733, https://openalex.org/W2566620708, https://openalex.org/W3028141363, https://openalex.org/W2045030989, https://openalex.org/W3098977020, https://openalex.org/W4242031852, https://openalex.org/W1987872348, https://openalex.org/W1524233929, https://openalex.org/W2148034739, https://openalex.org/W4233026002, https://openalex.org/W2594318146
referenced_works_count	33
abstract_inverted_index.a	18, 55, 88, 99, 249
abstract_inverted_index.VA	162
abstract_inverted_index.We	12
abstract_inverted_index.an	94, 209
abstract_inverted_index.as	248
abstract_inverted_index.at	54
abstract_inverted_index.in	67, 93, 125, 213, 217
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abstract_inverted_index.of	27, 46, 137, 147, 174, 184, 211
abstract_inverted_index.to	6, 14, 22, 29, 39, 252
abstract_inverted_index.520	50
abstract_inverted_index.AUC	112, 136, 173
abstract_inverted_index.PKU	153
abstract_inverted_index.The	58, 74
abstract_inverted_index.and	4, 16, 72, 78, 87, 98, 120, 161, 190, 196, 215, 230, 235
abstract_inverted_index.for	243
abstract_inverted_index.its	219
abstract_inverted_index.the	24, 43, 110, 126, 135, 138, 148, 172, 175, 197, 203, 206, 236
abstract_inverted_index.was	37, 62, 76, 141, 180
abstract_inverted_index.(200	168
abstract_inverted_index.(583	131
abstract_inverted_index.(95%	113, 116
abstract_inverted_index.7.8%	216
abstract_inverted_index.CADx	198, 237
abstract_inverted_index.MPNs	28, 53, 244
abstract_inverted_index.Mayo	157
abstract_inverted_index.from	49
abstract_inverted_index.help	253
abstract_inverted_index.than	145, 182
abstract_inverted_index.that	134, 171, 183
abstract_inverted_index.this	239
abstract_inverted_index.used	38
abstract_inverted_index.were	70
abstract_inverted_index.with	52, 80, 208, 233
abstract_inverted_index.(SPN)	84
abstract_inverted_index.0.727	191
abstract_inverted_index.0.741	188
abstract_inverted_index.0.890	142
abstract_inverted_index.0.909	118
abstract_inverted_index.1,739	47
abstract_inverted_index.14.3%	212
abstract_inverted_index.Brock	149
abstract_inverted_index.CI)),	117
abstract_inverted_index.PKU-M	103, 139, 176
abstract_inverted_index.[area	108
abstract_inverted_index.aimed	13
abstract_inverted_index.guide	30
abstract_inverted_index.model	21, 59, 75, 104, 140, 150, 154, 158, 163, 177, 204, 242
abstract_inverted_index.novel	240
abstract_inverted_index.solid	246
abstract_inverted_index.those	146
abstract_inverted_index.under	109
abstract_inverted_index.using	42, 64, 221, 226
abstract_inverted_index.which	68
abstract_inverted_index.(Brier	122
abstract_inverted_index.(CADx)	91
abstract_inverted_index.(PKU-M	60
abstract_inverted_index.0.122)	124
abstract_inverted_index.[0.682	164
abstract_inverted_index.[0.739	159
abstract_inverted_index.[0.748	194
abstract_inverted_index.[0.757	200
abstract_inverted_index.[0.780	155
abstract_inverted_index.[0.790	186
abstract_inverted_index.[0.806	151
abstract_inverted_index.[0.871	178
abstract_inverted_index.cohort	97
abstract_inverted_index.curve;	111
abstract_inverted_index.higher	144, 181
abstract_inverted_index.model)	61
abstract_inverted_index.nodule	83
abstract_inverted_index.score,	123
abstract_inverted_index.showed	105, 170
abstract_inverted_index.system	92, 199
abstract_inverted_index.(MPNs).	11
abstract_inverted_index.10-fold	65
abstract_inverted_index.Chinese	56
abstract_inverted_index.boosted	33
abstract_inverted_index.center.	57
abstract_inverted_index.cohort.	128
abstract_inverted_index.develop	15
abstract_inverted_index.machine	19, 222
abstract_inverted_index.models,	85
abstract_inverted_index.nodules	10, 48
abstract_inverted_index.predict	40
abstract_inverted_index.system,	238
abstract_inverted_index.trained	63
abstract_inverted_index.External	129
abstract_inverted_index.cohorts,	229
abstract_inverted_index.compared	79
abstract_inverted_index.critical	5
abstract_inverted_index.diagnose	7
abstract_inverted_index.ensemble	34
abstract_inverted_index.estimate	23
abstract_inverted_index.increase	210
abstract_inverted_index.interval	115
abstract_inverted_index.learning	223
abstract_inverted_index.multiple	8
abstract_inverted_index.nodules)	132, 169
abstract_inverted_index.patients	51
abstract_inverted_index.revealed	133
abstract_inverted_index.selected	71
abstract_inverted_index.solitary	81
abstract_inverted_index.surgeons	185
abstract_inverted_index.validate	17
abstract_inverted_index.(XGBoost)	36
abstract_inverted_index.algorithm	35
abstract_inverted_index.diagnosis	90
abstract_inverted_index.excellent	106
abstract_inverted_index.malignant	25
abstract_inverted_index.presented	245
abstract_inverted_index.pulmonary	9, 82
abstract_inverted_index.reference	251
abstract_inverted_index.validated	77
abstract_inverted_index.variables	45
abstract_inverted_index.clinicians	207, 234
abstract_inverted_index.comparison	167, 232
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abstract_inverted_index.convenient	250
abstract_inverted_index.evaluation	1
abstract_inverted_index.malignancy	41
abstract_inverted_index.prediction	241
abstract_inverted_index.validation	130, 225
abstract_inverted_index.Prospective	166
abstract_inverted_index.algorithms,	224
abstract_inverted_index.calibration	121
abstract_inverted_index.challenging	3
abstract_inverted_index.clinicians,	86
abstract_inverted_index.development	127, 220
abstract_inverted_index.fine-tuned.	73
abstract_inverted_index.independent	95
abstract_inverted_index.performance	247
abstract_inverted_index.probability	26
abstract_inverted_index.prospective	100, 231
abstract_inverted_index.radiologist	193
abstract_inverted_index.sensitivity	214
abstract_inverted_index.Furthermore,	202
abstract_inverted_index.multicentric	101, 228
abstract_inverted_index.outperformed	205
abstract_inverted_index.transnational	96, 227
abstract_inverted_index.computer-aided	89
abstract_inverted_index.discrimination	107
abstract_inverted_index.hyperparameters	69
abstract_inverted_index.(0.662–0.804),	189
abstract_inverted_index.(0.711–0.852),	187
abstract_inverted_index.(0.815–0.915)]	179
abstract_inverted_index.(0.854–0.946)]	119
abstract_inverted_index.(0.859–0.916),	143
abstract_inverted_index.cross-validation	66
abstract_inverted_index.learning–based	20
abstract_inverted_index.(0.640–0.722)].	165
abstract_inverted_index.(0.650–0.788)],	192
abstract_inverted_index.(0.671–0.814)],	195
abstract_inverted_index.(0.682–0.818)].	201
abstract_inverted_index.(0.697–0.776)],	160
abstract_inverted_index.(0.743–0.817)],	156
abstract_inverted_index.(0.771–0.838)],	152
abstract_inverted_index.Design:<p>A	32
abstract_inverted_index.clinicoradiologic	44
abstract_inverted_index.cohort.</p>Results:<p>The	102
abstract_inverted_index.decision-making.</p></div>	254
abstract_inverted_index.decision-making.</p>Experimental	31
abstract_inverted_index.<div>AbstractPurpose:<p>Nodule	0
abstract_inverted_index.specificity.</p>Conclusions:<p>After	218
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countries_distinct_count	2
institutions_distinct_count	26
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