Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Li, Tianhao; Huang, Honghong; Zhang, Shuocun; Zhang, Yongdan; Jing, Haoren; Sun, Tiansheng; Lu, Lidan; Zhang, Mingqing

doi:10.3389/fpubh.2022.984750

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…The lung is the second most frequently involved metastatic site, observed in around 10-15% of all metastasis cases, followed by the bone (detected in 1.2-12% CRC patients) and brain (incidence between 0.3 and 9%) [15][16][17]. Despite all metastases resulting in the poor median survival of patients with CRC, this varies by metastasis site as follows: liver (5 to 20 months), lung (5.9 to 31.2 months), bone (5 to 21 months), and brain (1 to 2 months) [17][18][19][20]. The five-year survival rates for these metastases are 15.99% for the liver, 16.70% for the lung, 5.51% for the brain, and less than 5% for bone metastasis [17,19].…”

Section: Introductionmentioning

confidence: 99%

“…Despite all metastases resulting in the poor median survival of patients with CRC, this varies by metastasis site as follows: liver (5 to 20 months), lung (5.9 to 31.2 months), bone (5 to 21 months), and brain (1 to 2 months) [17][18][19][20]. The five-year survival rates for these metastases are 15.99% for the liver, 16.70% for the lung, 5.51% for the brain, and less than 5% for bone metastasis [17,19]. These observations mentioned above show the prognosis of metastasis in CRC, particularly its spread to vital organs.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Ajithkumar,

Vasantharajan,

Pattison

et al. 2024

IJMS

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Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Ajithkumar,

Vasantharajan,

Pattison

et al. 2024

IJMS

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“…Since the emergence of big data analysis and machine learning (ML), it is possible to provide an alternative option for factors screening. There have been several predictive models with outstanding performance being built to apply in clinical practice by using big data and ML (14)(15)(16). The Surveillance, Epidemiology, and End Results (SEER) database (https://seer.cancer.gov/) covers geographically diverse patients with detailed information on the patients' clinicopathological statistics and follow-up visits, providing an abundance of medical cases to analyze.…”

Section: Introductionmentioning

confidence: 99%

Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

Tang

et al. 2023

Front. Oncol.

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BackgroundThis study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).MethodsPatients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.ResultsFor the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.ConclusionThe ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.

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“…The general purpose of clinical AI is to find relevant information from complex and high-dimensional data to assist decision-making. 2 Clinical AI should be useful to solve several clinical tasks such as diagnosis, 3 , 4 , 5 disease stratification, 6 risk predictions, 7 , 8 therapeutic decisions, 9 prognostic predictions, 10 , 11 and drug discovery. 12 …”

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confidence: 99%

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

Koçak¹,

Cuocolo²,

Santos³

et al. 2023

Balkan Med J

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In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.

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Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Cited by 6 publications

References 35 publications

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

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