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The increasing volume of collected cancer imaging data, together with the development of technological tools based on Artificial Intelligence (AI), offers unprecedented opportunities for enhancing cancer management and improving clinical workflows. This chapter explores the approaches adopted by two projects within the AI for Health Imaging (AI4HI) cluster, INCISIVE and ProCancer-I, targeting various cancer types. In total, sixteen models were implemented across two projects, focusing on prostate, breast, lung, and ovarian cancers. These models were designed for lesion segmentation, patient stratification, and predicting metastasis risk or radiotherapy side effects, utilizing various DL and ML architectures such as YOLO, ResUnet++, and U-Net. Diverse imaging modalities were used, including Magnetic Resonance Imaging, Computed Tomography, and Mammography, while whole-slide images were used for the detection and classification of cell types in histopathological images. Radiomics was employed for classification and prediction by extracting features from imaging data, with harmonization techniques applied to improve model generalizability. Although some models incorporated clinical data, most relied on imaging features, highlighting the potential for improved performance by integrating multimodal data. To further enhance model performance and generalizability, comprehensive repositories with detailed clinical and follow-up data are needed. Additionally, addressing model fairness, explainability, and biological validation is essential for gaining acceptance within the clinical community.

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AI Models in Cancer Diagnosis and Prognosis

  • 章节

  • 发表日期：

    2025年

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  • Dimitris Filos,
  • Stavros Sykiotis,
  • Ioannis Tzortzis,
  • Ioannis Rallis,
  • Anastasios Doulamis,
  • Nikolaos Doulamis,
  • José Guilherme de Almeida,
  • Nuno Rodrigues,
  • Ana Carolina Rodrigues,
  • Nickolas Papanikolaou,
  • Tatjana Loncar-Turukalo,
  • Niksa Jakovljevic,
  • Ivan Lazic,
  • Milan Rapaic,
  • Sampsa Hautaniemi,
  • Claudia Caudai,
  • Giulio Del Corso,
  • Danila Germanese,
  • Eva Pachetti,
  • Maria Antonietta Pascali,
  • Sara Colantonio,
  • Ioanna Chouvarda

摘要

The increasing volume of collected cancer imaging data, together with the development of technological tools based on Artificial Intelligence (AI), offers unprecedented opportunities for enhancing cancer management and improving clinical workflows. This chapter explores the approaches adopted by two projects within the AI for Health Imaging (AI4HI) cluster, INCISIVE and ProCancer-I, targeting various cancer types. In total, sixteen models were implemented across two projects, focusing on prostate, breast, lung, and ovarian cancers. These models were designed for lesion segmentation, patient stratification, and predicting metastasis risk or radiotherapy side effects, utilizing various DL and ML architectures such as YOLO, ResUnet++, and U-Net. Diverse imaging modalities were used, including Magnetic Resonance Imaging, Computed Tomography, and Mammography, while whole-slide images were used for the detection and classification of cell types in histopathological images. Radiomics was employed for classification and prediction by extracting features from imaging data, with harmonization techniques applied to improve model generalizability. Although some models incorporated clinical data, most relied on imaging features, highlighting the potential for improved performance by integrating multimodal data. To further enhance model performance and generalizability, comprehensive repositories with detailed clinical and follow-up data are needed. Additionally, addressing model fairness, explainability, and biological validation is essential for gaining acceptance within the clinical community.