Integration of platelet features in blood and platelet rich plasma for detection of lung cancer

Zu, Ruiling; Yu, Sisi; Yang, Guishu; Ge, Yiman; Wang, Dongsheng; Zhang, Li; Song, Xiaoyu; Deng, Yao; He, Qiao; Zhang, Kaijiong; Huang, Jian; Luo, Huaichao

doi:10.1016/j.cca.2020.05.043

Cited by 14 publications

(12 citation statements)

References 36 publications

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“…Patients should be observed for the duration of the diagnosis and therapy line (up to the day the patient discharged from hospital). The blood samples were collected and determined at the same time participants admitted hospital 12 . The pathological examination of resected tissue was finished after surgery or biopsy.…”

Section: Methodsmentioning

confidence: 99%

“…Routine blood test (RBT) is a low-cost test including more than dozens of features and revealing the complications of individuals, which can be characterized as an easy and basic liquid biopsy and provides a large data for researches 11 . In our previous research, we identified that platelet features based on RBT had potential to diagnose lung cancer, which meant the platelet features could complement LDCT to distinguish benign nodules from malignant nodules 12 . In this research, we collected real-world data to construct a diagnostic model combining platelet features with clinical and LDCT information, which could be used to discriminate malignancy from benign nodules, and to expand the treatment options.…”

Section: Introductionmentioning

confidence: 99%

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A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data

Zu¹,

Wu²,

Zhou³

et al. 2022

J. Cancer

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Objectives: As the pulmonary nodules were hard to be discriminated as benignancy or malignancy only based on imageology, a prospective and observational real-world research was devoted to develop and validate a predictive model for managing the diagnostic challenge. Methods: This study started in 2018, and a predictive model was constructed using eXtreme Gradient Boosting (XGBoost) based on computed tomographic, clinical, and platelet data of all the eligible patients. And the model was evaluated and compared with other common models using ROC curves, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and net benefit (NB). Subsequently, the model was validated in an external cohort. Results: The development group included 419 participants, while there were 62 participants in the external validation cohort. The most accurate XGBoost model called SCHC model including age, platelet counts in platelet rich plasma samples (pPLT), plateletcrit in platelet rich plasma samples (pPCT), nodule size, and plateletcrit in whole blood samples (bPCT). In the development group, the SCHC model performed well in whole group and subgroups. Compared with VA, MC, BU model, the SCHC model had a significant improvement in reclassification as assessed by the NRI and IDI, and could bring the patients more benefits. For the external validation, the model performed not as well. The algorithm of SCHC, VA, MC, and BU model were first integrated using a web tool ( http://i.uestc.edu.cn/SCHC ). Conclusions: In this study, a platelet feature-based model could facilitate the discrimination of early-stage malignancy from benignancy patients, to ensure accurate diagnosis and optimal management. This research also indicated that common laboratory results also had the potential in diagnosing cancers.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data

Zu¹,

Wu²,

Zhou³

et al. 2022

J. Cancer

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“…Studies indicated that platelets from cancer patients altered their growth factor contents (65)(66)(67), RNA profile (68)(69)(70)(71) and other parameters including platelet counts, volumes, and protein contents (72) in early stage.…”

Section: Platelet Transcriptome and Proteome Alterationmentioning

confidence: 99%

Bidirectional Interaction Between Cancer Cells and Platelets Provides Potential Strategies for Cancer Therapies

Guo

Chang

et al. 2021

Front. Oncol.

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Platelets are essential components in the tumor microenvironment. For decades, clinical data have demonstrated that cancer patients have a high risk of thrombosis that is associated with adverse prognosis and decreased survival, indicating the involvement of platelets in cancer progression. Increasing evidence confirms that cancer cells are able to induce production and activation of platelets. Once activated, platelets serve as allies of cancer cells in tumor growth and metastasis. They can protect circulating tumor cells (CTCs) against the immune system and detachment-induced apoptosis while facilitating angiogenesis and tumor cell adhesion and invasion. Therefore, antiplatelet agents and platelet-based therapies should be developed for cancer treatment. Here, we discuss the mechanisms underlying the bidirectional cancer-platelet crosstalk and platelet-based therapeutic approaches.

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“…However, the model failed to validate the lung cancer prediction with larger external data set. In [11], integration of platelet features and platelet-rich plasma were extracted for lung cancer identification. But the feature analysis was not done to improve the accuracy of lung cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction

Thaventhiran¹,

Sekar²

2022

Intelligent Automation &Amp; Soft Computing

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Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given input patient data. Different processes are carried out at each layer to predict lung cancer at an earlier stage. The input layer of the deep neural network receives the data and associated features and sends them to the hidden layer. The first hidden layer performs the feature selection process using Target Projection matching to identify the relevant features for accurate disease prediction with minimum time consumption. Hidden layer 2 performs the patient Data Classification based on Czekanowski's dice similarity coefficient with the selected relevant features from the previous layer to predict lung cancer. The factors considered for performance evaluation of the proposed technique with the existing state of the art approaches include prediction accuracy, false-positive rate and prediction time. Lunar 16 Lung Cancer dataset consisting of patient data is used for evaluation. The obtained results show that the proposed TPFMDANN-LSTM technique achieves higher prediction accuracy with minimum time consumption and less false positive rate than the state-of-the-art methods. The experimental results reveal that the TPFMDANN-LSTM technique performs better with a 6% improvement in prediction accuracy, 36% reduction of false positives, and 16% faster prediction time for lung cancer detection compared to existing works.

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Integration of platelet features in blood and platelet rich plasma for detection of lung cancer

Cited by 14 publications

References 36 publications

A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data

A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data

Bidirectional Interaction Between Cancer Cells and Platelets Provides Potential Strategies for Cancer Therapies

Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction

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