Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry Study

Lee, Hsi-Chieh; Lin, Tsung-Chieh; Chang, Chi‐Chang; Lu, Yen-Chiao Angel; Lee, Chih-Min; Purevdorj, Bolormaa

doi:10.3390/app122412520

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…In this approach, the AGEbRF mechanism is developed for segmenting and predicting skin cancer. The developed GEbRF is the combined form of the Golden Eagle Optimization (GEO) algorithm ( Mohammadi-Balani et al, 2021 ) and random forest (RF) ( Lee et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

Balaji

Hùng

Chakrabarti

et al. 2023

PeerJ Computer Science

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One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system’s training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research’s parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.

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Section: Methodsmentioning

confidence: 99%

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

Balaji

Hùng

Chakrabarti

et al. 2023

PeerJ Computer Science

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“…[ Lee et al 2022] focused on assessing the risk of developing a second primary cutaneous tumor in skin cancer survivors, analyzing data from 1248 patients across five cancer registries. The researchers applied various machine learning algorithms, notably achieving optimal results with Random Forest.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Skin Tumor Invasiveness: A National Analysis Through Explainable Artificial Intelligence (XAI)

Mata,

Leitão Júnior

2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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In Brazil, skin tumors represents the type of neoplasm with the highest incidence rate among the population. Because of this, this study explores the invasiveness of this disease using computational techniques to understand how specific patient characteristics influence its progression. Through the analysis of data provided by the Cancer Hospital Registry (RHC) of the National Cancer Institute José Alencar Gomes da Silva (INCA), and with the aid of Artificial Intelligence (AI) algorithms explained by the SHapley Additive exPlanations (SHAP) approach, the study reveals that the invasiveness of skin cancer is affected in a significantly different way by the individual characteristics of patients compared to analyses based on more general attributes. These findings underline the importance of personalization in medicine, suggesting that a deeper understanding of individual characteristics can lead to more accurate diagnoses and more effective treatments. Furthermore, the research highlights the role of XAI in clarifying these relationships, pointing to the need for more refined approaches in prevention, treatment, and the formulation of public health policies aimed at combating skin tumors, despite limitations such as data imbalance encountered during the study.

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Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer

Hsia,

Chang,

Liu

et al. 2024

Diagnostics

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Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

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Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry Study

Cited by 3 publications

References 29 publications

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

Prediction of Skin Tumor Invasiveness: A National Analysis Through Explainable Artificial Intelligence (XAI)

Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer

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