Artificial intelligence based personalized predictive survival among colorectal cancer patients

Susič, David; Syed-Abdul, Shabbir; Dovgan, Erik; Jonnagaddala, Jitendra; Gradišek, Anton

doi:10.1016/j.cmpb.2023.107435

Cited by 11 publications

(10 citation statements)

References 22 publications

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“…These algorithms have the capacity to consider a wide range of variables and discover hidden correlations that may not be apparent through traditional methods, offering a more comprehensive and personalized approach to colorectal cancer treatment. 215,216 Furthermore, AI and machine learning can assist in the identification of new drug targets and development of novel therapies. By mining extensive databases of biological information, these technologies can uncover potential drug candidates and predict their efficacy in specific patient populations 12 (Figure 8).…”

Section: Future Directions and Emerging Technologiesmentioning

confidence: 99%

“…Machine learning algorithms are also being used to integrate and analyze vast data sets, including genetic, molecular, clinical, and radiological information. , By identifying complex patterns within this data, machine learning can help clinicians predict patient prognosis, identify potential treatment targets, and tailor therapies to individual patients. These algorithms have the capacity to consider a wide range of variables and discover hidden correlations that may not be apparent through traditional methods, offering a more comprehensive and personalized approach to colorectal cancer treatment. , …”

Section: Future Directions and Emerging Technologiesmentioning

confidence: 99%

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Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Kiran,

Yashaswini,

Maheshwari

et al. 2024

ACS Pharmacol. Transl. Sci.

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Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

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Section: Future Directions and Emerging Technologiesmentioning

confidence: 99%

Section: Future Directions and Emerging Technologiesmentioning

confidence: 99%

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Kiran,

Yashaswini,

Maheshwari

et al. 2024

ACS Pharmacol. Transl. Sci.

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“…Furthermore, in prognostic research related to colorectal cancer, Susič and colleagues employed machine learning algorithms to predict 1-5 year survival rates in colorectal cancer patients, attempting to identify crucial variables influencing patient survival. 18 Wang et al established a nomogram model based on multi-omics features including pathological, radiological, and immunological characteristics for predicting prognosis of colorectal cancer lung metastasis, demonstrating favorable clinical utility. 19 By directly predicting diagnosis, treatment effectiveness, and prognosis from pathological slides, healthcare can be more precisely implemented, making it more efficient.…”

Section: Introductionmentioning

confidence: 99%

“…These studies suggest that AI holds promise in offering valuable insights into the tumor microenvironment of colorectal cancer. Furthermore, in prognostic research related to colorectal cancer, Susič and colleagues employed machine learning algorithms to predict 1–5 year survival rates in colorectal cancer patients, attempting to identify crucial variables influencing patient survival 18 . Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Tan,

Liu,

Xue

et al. 2024

Cancer Medicine

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ObjectiveThis retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI‐assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments.MethodsThis study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA).ResultsA total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model.ConclusionThe combined model and nomogram, which integrate pathomics multi‐modal data and clinical‐pathological variables, demonstrated superior performance in distinguishing between Stage I–II and Stage III colorectal cancer. This provides valuable support for clinical decision‐making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

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“…In gastric cancer, the literature reported seven machine learning algorithms to predict distant metastasis models, including logistic regression, random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, support vector machine, k-nearest neighbor, naive Bayes model, and artificial neural network (8). David's research used 11 machine learning algorithms to predict the short-and longterm survival probability of CRC patients (9).…”

Section: Introductionmentioning

confidence: 99%

A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features

Cao

Ding

et al. 2023

Front. Oncol.

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ObjectiveColorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups.MethodsWe retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People’s Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA).ResultsA total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People’s Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level (p = 0.009), carcinoembryonic antigen (CEA) level (p = 0.032), histological grade (p < 0.001), colorectal cancer tumor markers (p < 0.001), N stage (p < 0.001), and tumor site (p = 0.005) were associated with distant metastasis in colorectal cancer patients. Multivariate logistic regression analysis showed that N stage (p < 0.001), histological grade (p = 0.026), and colorectal cancer markers (p < 0.001) were independent predictors of distant metastasis in patients initially diagnosed with colorectal cancer. The above six risk factors were used to predict distant metastasis of newly diagnosed colorectal cancer. The C-indexes for the prediction of the nomogram were 0.902 (95% confidence interval (CI), 0.857–0.948).ConclusionThe nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making.

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Artificial intelligence based personalized predictive survival among colorectal cancer patients

Cited by 11 publications

References 22 publications

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features

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