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Correction: Plasma extracellular vesicle phenotyping for the differentiation of early-stage lung cancer and benign lung diseases
Liwen
Yuan
a,
Yanpin
Chen
b,
Longfeng
Ke
c,
Quan
Zhou
d,
Jiayou
Chen
e,
Min
Fan
a,
Alain
Wuethrich
*d,
Matt
Trau
*df and
Jing
Wang
*a
aKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350117, China. E-mail: jing.wang@fjnu.edu.cn
bDepartment of Pathology, Clinical Oncology School of Fujian Medical University, and Fujian Cancer Hospital, Fuzhou, Fujian 350014, China
cLaboratory of Molecular Pathology, Clinical Oncology School of Fujian Medical University and Fujian Cancer Hospital, Fuzhou, Fujian 350014, China
dCentre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia. E-mail: a.wuethrich@uq.edu.au; m.trau@uq.edu.au
eDepartment of Radiology, Clinical Oncology School of Fujian Medical University and Fujian Cancer Hospital, Fuzhou, Fujian 350014, China
fSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia
First published on 14th July 2023
Abstract
Correction for ‘Plasma extracellular vesicle phenotyping for the differentiation of early-stage lung cancer and benign lung diseases’ by Liwen Yuan et al., Nanoscale Horiz., 2023, 8, 746–758, https://doi.org/10.1039/d2nh00570k.
The authors regret that an incorrect version of Fig. 6E and G was included in the originally published article. The correct version of Fig. 6 is shown below.
 | ||
| Fig. 1 Clinical sample analysis. (A) SERS measurements of target marker levels on the surface of plasma EVs among clinical groups. (B) Heat map results representing the EV SERS signature of individuals. (C) LDA discriminant scores obtained from a discriminant model for classifying 26 early-stage lung cancer patients and 21 patients with benign lung diseases. (D) LDA discriminant scores obtained from a discriminant model for differentiating 28 early-stage lung cancer patients and controls consisting of 23 patients with benign lung diseases and 26 healthy controls. (E) The clinical detection sensitivity and specificity of our nanostrategy in combination with machine learning algorithms (LDA, PLS-DA, SVM, MLR, or KNN) and a CT scan differentiating 26 early-stage lung cancer patients and 21 patients with benign lung diseases. ROC curves reflected the diagnostic ability of our nanostrategy in combination with different machine learning algorithms for the differentiation of (F) early-stage lung cancer and benign lung diseases, and (G) early-stage lung cancer and controls (benign lung diseases and healthy controls). Data in (A) are represented as mean ± standard deviation, where error bars represent the standard deviation of biological replicates. ****P < 0.0001; ns, not significant. | ||
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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