Leveraging machine learning to predict de novo skin malignancy following lung transplantation

Nasim Nosoudi; Amir Hassan Zadeh; Rayna Nichols; Cameron Kiani; Lilliana Polo-Corrales

doi:10.1080/17581966.2026.2617115

January 19, 2026Lung Cancer ManagementOpen Access

Leveraging machine learning to predict de novo skin malignancy following lung transplantation

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Key Points

To explore the use of machine learning for predicting skin malignancy risk after lung transplantation.
Developed a predictive model using machine learning techniques
Analyzed data on lung transplant recipients
Assessed risks for post-transplant malignancies
Identified key risk factors for skin malignancy
Enabled personalized assessments for transplant recipients
Supported targeted surveillance strategies for early detection

Implication

Predictive modeling shows malignancy risk in lung transplant recipients, suggesting improved surveillance strategies.

Abstract

ML-driven predictive modeling enables personalized assessment of post-transplant malignancy risk, supporting early detection, targeted surveillance, and optimized long-term care for lung transplant recipients.

Discussion

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Cite This Study

Nosoudi et al. (2026) studied this question.

synapsesocial.com/papers/69706c87b6488063ad5c18b3 https://doi.org/10.1080/17581966.2026.2617115

View Full Paper

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Leveraging machine learning to predict de novo skin malignancy following lung transplantation

Key Points

Implication

Abstract

Discussion

Cite This Study

Leveraging machine learning to predict de novo skin malignancy following lung transplantation

Key Points

Implication

Abstract

Discussion

Cite This Study

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