Corrigendum to “Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition: A preliminary study” [Comput Methods Programs Biomed. 163 (2018) 33–38]

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

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Classification of lung cancer subtypes by data mining technique

Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) ◽

10.1109/ciec.2014.6959151 ◽

2014 ◽

Cited By ~ 8

Author(s):

M. Venkat Dass ◽

Mohammed Abdul Rasheed ◽

Mohammed Mahmood Ali

Keyword(s):

Lung Cancer ◽

Data Mining ◽

Data Mining Technique ◽

Cancer Subtypes ◽

Mining Technique

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Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Cancers ◽

10.3390/cancers12061604 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1604 ◽

Cited By ~ 1

Author(s):

Mark Kriegsmann ◽

Christian Haag ◽

Cleo-Aron Weis ◽

Georg Steinbuss ◽

Arne Warth ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Test Set ◽

Cancer Subtypes ◽

Morphological Evaluation ◽

Therapy Stratification

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.

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