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Artificial intelligence in cancer imaging: Clinical challenges and applications. ​

Literature Information ​

DOI10.3322/caac.21552
PMID30720861
JournalCA: a cancer journal for clinicians
Impact Factor232.4
JCR QuartileQ1
Publication Year2019
Times Cited756
Keywordsartificial intelligence, cancer imaging, clinical challenges, deep learning, radiomics
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
ISSN0007-9235
Pages127-157
Issue69(2)
AuthorsWenya Linda Bi, Ahmed Hosny, Matthew B Schabath, Maryellen L Giger, Nicolai J Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F Dunn, Raymond H Mak, Rulla M Tamimi, Clare M Tempany, Charles Swanton, Udo Hoffmann, Lawrence H Schwartz, Robert J Gillies, Raymond Y Huang, Hugo J W L Aerts

TL;DR ​

This study reviews the application of artificial intelligence (AI) in the medical imaging of cancer, highlighting its potential to enhance the qualitative interpretation of images, improve tumor monitoring, and inform treatment decisions across various tumor types. While challenges remain regarding the validation of AI models, the findings underscore a growing commitment to integrating AI into oncology to transform clinical workflows and ultimately improve cancer care.

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artificial intelligence · cancer imaging · clinical challenges · deep learning · radiomics

Abstract ​

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Primary Questions Addressed ​

  1. What are the specific challenges faced by clinicians when integrating AI into cancer imaging practices?
  2. How does the use of AI in cancer imaging differ across various tumor types, such as lung, brain, breast, and prostate cancers?
  3. What advancements in AI technology are most promising for improving the accuracy of cancer detection and monitoring?
  4. In what ways can AI enhance the personalization of treatment plans based on imaging data in oncology?
  5. What are the implications of AI's role in shifting clinical workflows for radiographic detection and management decisions in cancer care?

Key Findings ​

Background and Purpose ​

The paper discusses the integration of artificial intelligence (AI) in cancer imaging, addressing the clinical challenges and potential applications. Despite advancements in imaging technology, accurate detection and monitoring of cancers remain complex due to the heterogeneity of tumors and individual patient factors. The goal is to explore how AI can enhance the interpretation of imaging data to improve cancer care.

Main Methods/Materials/Experimental Design ​

The authors outline the methodologies and applications of AI in imaging across four major cancer types: lung, brain, breast, and prostate. They categorize AI applications into three main clinical tasks: detection, characterization, and monitoring of tumors.

Mermaid diagram
  1. Detection: Involves using AI to identify tumors in imaging scans, improving the accuracy of initial screenings.
  2. Characterization: AI aids in determining tumor types and staging through advanced imaging analyses, including radiomics, which quantifies image features.
  3. Monitoring: AI enhances the ability to track tumor changes over time, improving treatment decision-making and patient management.

Key Results and Findings ​

  • AI tools show promise in enhancing detection rates of tumors, particularly in lung cancer, where they can differentiate between benign and malignant nodules effectively.
  • Characterization through AI can predict tumor aggressiveness and assist in staging, which is crucial for treatment planning.
  • In breast cancer, AI can analyze mammographic density and parenchymal patterns to assess risk and prognosis.
  • The integration of AI in prostate cancer imaging demonstrates improved localization and characterization of lesions, leading to better diagnostic accuracy.

Main Conclusions/Significance/Innovation ​

The study emphasizes the transformative potential of AI in cancer imaging, advocating for its integration into clinical workflows to enhance diagnostic accuracy and patient outcomes. AI not only improves the quality of imaging interpretation but also facilitates personalized treatment approaches through better risk stratification and monitoring.

Limitations and Future Directions ​

Despite the promising results, the authors acknowledge several limitations:

  • Many AI applications are still in the preclinical phase and require rigorous validation for reproducibility and generalizability.
  • There are challenges related to data curation, including the need for well-annotated datasets for training AI models.
  • Ethical and regulatory concerns regarding AI in healthcare need to be addressed to ensure accountability and transparency.

Future research should focus on expanding AI applications in clinical settings, developing standardized protocols for data collection, and enhancing the interpretability of AI models to foster trust among clinicians and patients.

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Literatures Citing This Work ​

  1. Open access image repositories: high-quality data to enable machine learning research. - F Prior;J Almeida;P Kathiravelu;T Kurc;K Smith;T J Fitzgerald;J Saltz - Clinical radiology (2020)
  2. An overview of GeoAI applications in health and healthcare. - Maged N Kamel Boulos;Guochao Peng;Trang VoPham - International journal of health geographics (2019)
  3. Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer. - Fei Kang;Wei Mu;Jie Gong;Shengjun Wang;Guoquan Li;Guiyu Li;Wei Qin;Jie Tian;Jing Wang - European journal of nuclear medicine and molecular imaging (2019)
  4. Predicting progression of in situ carcinoma in the era of precision genomics. - Monica Cheng;Nasser H Hanna;Darrell D Davidson;Richard B Gunderman - Journal of thoracic disease (2019)
  5. Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children. - Bei Wang;Min Li;He Ma;Fangfang Han;Yan Wang;Shunying Zhao;Zhimin Liu;Tong Yu;Jie Tian;Di Dong;Yun Peng - BMC medical imaging (2019)
  6. Association of radiomic imaging features and gene expression profile as prognostic factors in pancreatic ductal adenocarcinoma. - Ke Li;Jingjing Xiao;Jiali Yang;Meng Li;Xuanqi Xiong;Yongjian Nian;Linbo Qiao;Huaizhi Wang;Aydin Eresen;Zhuoli Zhang;Xianling Hu;Jian Wang;Wei Chen - American journal of translational research (2019)
  7. Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine. - Houman Sotoudeh;Omid Shafaat;Joshua D Bernstock;Michael David Brooks;Galal A Elsayed;Jason A Chen;Paul Szerip;Gustavo Chagoya;Florian Gessler;Ehsan Sotoudeh;Amir Shafaat;Gregory K Friedman - Frontiers in oncology (2019)
  8. Artificial Intelligence in Imaging: The Radiologist's Role. - Daniel L Rubin - Journal of the American College of Radiology : JACR (2019)
  9. Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma. - Bin Chen;Lianzhen Zhong;Di Dong;Jianjun Zheng;Mengjie Fang;Chunyao Yu;Qi Dai;Liwen Zhang;Jie Tian;Wei Lu;Yinhua Jin - Frontiers in oncology (2019)
  10. "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era. - Kendall J Kiser;Benjamin D Smith;Jihong Wang;Clifton D Fuller - Frontiers in oncology (2019)

... (746 more literatures)

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