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3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility
(2022-04-01)
Puangragsa, Utumporn
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Setakornnukul, Jiraporn
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Dankulchai, Pittaya
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Phasukkit, Pattarapong
This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: The respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: Patient-based datasets with regular and irregular breathing patterns; and pseudopatientbased datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.
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Targeting multiple genes containing long mononucleotide A-T repeats in lung cancer stem cells
(2021-12-01)
Bhummaphan, Narumol
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Pin-on, Piyapat
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Phiboonchaiyanan, Preeyaporn Plaimee
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Siriluksana, Jirattha
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Aporntewan, Chatchawit
Background: Intratumour heterogeneous gene expression among cancer and cancer stem cells (CSCs) can cause failure of current targeted therapies because each drug aims to target the function of a single gene. Long mononucleotide A-T repeats are cis-regulatory transcriptional elements that control many genes, increasing the expression of numerous genes in various cancers, including lung cancer. Therefore, targeting A-T repeats may dysregulate many genes driving cancer development. Here, we tested a peptide nucleic acid (PNA) oligo containing a long A-repeat sequence [A(15)] to disrupt the transcriptional control of the A-T repeat in lung cancer and CSCs. Methods: First, we separated CSCs from parental lung cancer cell lines. Then, we evaluated the role of A-T repeat gene regulation by counting the number of repeats in differentially regulated genes between CSCs and the parental cells of the CSCs. After testing the dosage and effect of PNA-A15 on normal and cancer cell toxicity and CSC phenotypes, we analysed genome-wide expression to identify dysregulated genes in CSCs. Results: The number of A-T repeats in genes differentially regulated between CSCs and parental cells differed. PNA-A15 was toxic to lung cancer cells and CSCs but not to noncancer cells. Finally, PNA-A15 dysregulated a number of genes in lung CSCs. Conclusion: PNA-A15 is a promising novel targeted therapy agent that targets the transcriptional control activity of multiple genes in lung CSCs.
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Titania nanosheet generates peroxynitrite-dependent S-nitrosylation and enhances p53 function in lung cancer cells
(2021-08-01)
Soonnarong, Rapeepun
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Tungsukruthai, Sucharat
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Nutho, Bodee
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Rungrotmongkol, Thanyada
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Vinayanuwattikun, Chanida
Metal nanomaterials can enhance the efficacy of current cancer therapies. Here, we show that Ti<inf>0.8</inf> O<inf>2</inf> nanosheets cause cytotoxicity in several lung cancer cells but not in normal cells. The nanosheet-treated cells showed certain apoptosis characteristics. Protein analysis further indicated the activation of the p53-dependent death mechanism. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) analyses revealed the cellular uptake of the nanosheets and the induction of cell morphological change. The nanosheets also exhibited a substantial apoptosis effect on drug-resistant metastatic primary lung cancer cells, and it was found that the potency of the nanosheets was dramatically higher than standard drugs. Ti<inf>0.8</inf> O<inf>2</inf> nanosheets induce apoptosis through a molecular mechanism involving peroxynitrite (ONOO−) generation. As peroxynitrite is known to be a potent inducer of S-nitrosylation, we further found that the nanosheets mediated the S-nitrosylation of p53 at C182, resulting in higher protein-protein complex stability, and this was likely to induce the surrounding residues, located in the interface region, to bind more strongly to each other. Molecular dynamics analysis revealed that S-nitrosylation stabilized the p53 dimer with a ∆Gbindresidue of <−1.5 kcal/mol. These results provide novel insight on the apoptosis induction effect of the nanosheets via a molecular mechanism involving S-nitrosylation of the p53 protein, emphasizing the mechanism of action of nanomaterials for cancer therapy.
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Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest
(2020-04-01)
Paing, May Phu
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Hamamoto, Kazuhiko
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Tungjitkusolmun, Supan
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Visitsattapongse, Sarinporn
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Pintavirooj, Chuchart
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
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Automatic detection of mediastinal lymph nodes using 3D convolutional neural network
(2019-09-16)
Paing, May Phu
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Pintavirooj, Chuchart
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Tungjitkusolmun, Supan
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Win, Kyi Pyar
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Hamamoto, Kazuhiko
Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using greylevel thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
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Automatic detection and staging of lung tumors using locational features and double-staged classifications
(2019-06-01)
Paing, May Phu
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Hamamoto, Kazuhiko
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Tungjitkusolmun, Supan
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Pintavirooj, Chuchart
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

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