Molecular markers in cancer

Ahmad, Ejaj; Ali, Asgar; Nimisha,; Sharma, Abhay; Apurva,; Kumar, Arun; Mehdi, Ghulam; Sattar, Real Sumayya Abdul; Verma, Renu; Mahajan, Bhawna; Saluja, Sundeep Singh

doi:10.1016/j.cca.2022.05.029

Cited by 5 publications

(4 citation statements)

References 255 publications

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“…Blood tumor markers are one of the most commonly used screening methods for early-stage cancer. However, high levels are only present in some patients, and sensitivity is lacking in patients with low concentrations [ 21 ]. The diagnosis of cancer using a single test method has limitations, while comprehensive judgment based on clinical data extraction has great advantages.…”

Section: Discussionmentioning

confidence: 99%

“…Feature selection methods are used to remove redundant features from all available features to improve the efficiency of machine-learning models and reduce overfitting. Important feature combinations were effectively identified and validated by combining significance tests (

0.05), importance ranking of variables, and the minimum description length (MDL) algorithm [ 21 ]. This exploration of the importance of each feature in extracting data variables helps identify the optimal feature combination for model discrimination and improves model accuracy.…”

Section: Methodsmentioning

confidence: 99%

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A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

Qiu,

Chen,

Zhang

et al. 2024

THC

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BACKGROUND: The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial. OBJECTIVE: This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes. METHODS: This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm. RESULTS: Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%). CONCLUSION: The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

Qiu,

Chen,

Zhang

et al. 2024

THC

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“…Liquid biopsy supports the analysis of Circulating Tumor Cells (CTCs), ctDNA, circulating (or cell free) miRNAs (ctmiRNAs) and extracellular vehicles (EVs) that has greatly facilitated the early detection, prognosis, and design of tumor specific therapy thereby improving the overall management of cancer. Currently, liquid biopsy is one of the arms of many clinical trials that has improved our understanding of the metastatic process based on mechanistic and translational studies using CTC derived cell‐lines and explants (CDx) 79,80 …”

Section: Molecular Diagnostic Techniquesmentioning

confidence: 99%

“…Currently, liquid biopsy is one of the arms of many clinical trials that has improved our understanding of the metastatic process based on mechanistic and translational studies using CTC derived cell‐lines and explants (CDx). 79 , 80 …”

Section: Molecular Diagnostic Techniquesmentioning

confidence: 99%

Technological advancements in cancer diagnostics: Improvements and limitations

Pulumati

Dwarakanath

et al. 2023

Cancer Reports

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Background: Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low-and middle-income countries (LMICs).Recent Findings: Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop ® , LumaGEM ® , MarginProbe ® ) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary

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