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Bridging the Gap: Integrating AI into Clinical Practice for Oncological...
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Bridging the Gap: Integrating AI into Clinical Practice for Oncological PET/CT Imaging

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 1 April 2025 | Viewed by 2822

Special Issue Editor

Dr. Fereshteh Yousefirizi

E-Mail Website
Guest Editor
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
Interests: physician-in-the-loop; PET/CT; AI; foundation model; generalist model; tumor segmentation; outcome prediction; whole-body PET; cancers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to bridge the gap between AI algorithms developed in academia and the practical needs of clinical oncological PET/CT imaging. It addresses challenges such as model generalizability, economic feasibility, and regulatory constraints that limit the adoption of AI-based solutions in clinical settings. Additionally, it highlights the struggle of AI-based techniques developed in academic research labs to gain clinical trust compared to industrial solutions, which benefit from extensive datasets and clinical approvals.

AI promises significant advancements in diagnosis, treatment assessment, and planning, particularly in oncologic PET/CT imaging. However, challenges such as model generalizability, economic feasibility, and regulatory hurdles significantly hinder the widespread adoption of AI-based solutions in clinical settings. Techniques introduced by research labs often struggle to gain clinical trust compared to industrial solutions, which benefit from extensive datasets and clinical approvals. Moreover, the lack of agreed-upon problem statements and effective collaboration tools has impeded the integration of academic advancements into clinical practice.

This Special Issue seeks contributions from technical and clinical researchers to share their solutions and results within this framework. We are particularly interested in foundational and generalist medical AI models capable of performing diverse tasks using minimally labeled data through self-supervised and active learning. These models should be capable of interpreting various medical modalities, including imaging, electronic health records, lab results, genomics, graphs, and medical text, by leveraging large, diverse datasets. Specialists such as oncologists, nuclear medicine experts, and medical physicists will guide these models using specific prompts, ensuring that responses are informed by their expertise.

Dr. Fereshteh Yousefirizi
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • physician-in-the-loop
  • PET/CT
  • AI
  • foundation model
  • generalist model
  • tumor segmentation
  • outcome prediction
  • whole-body PET
  • cancers

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Published Papers (3 papers)

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13 pages, 3714 KiB  
Article
A Machine Learning-Based Radiomics Model for the Differential Diagnosis of Benign and Malignant Thyroid Nodules in F-18 FDG PET/CT: External Validation in the Different Scanner
by Junchae Lee, Jinny Lee and Bong-Il Song
Cancers 2025, 17(2), 331; https://doi.org/10.3390/cancers17020331 - 20 Jan 2025
Viewed by 448
Abstract
Background/Objectives: Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis [...] Read more.
Background/Objectives: Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis of TIs. Methods: A total of 152 patient cases were retrospectively analyzed and split into training and validation sets (7:3) using stratification and randomization. Results: The least absolute shrinkage and selection operator (LASSO) algorithm identified nine radiomics features from 960 candidates to construct a radiomics signature predictive of malignancy. Performance of the radiomics score was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). In the training set, the radiomics score achieved an AUC of 0.794 (95% CI: 0.703–0.885, p < 0.001). Validation was performed on internal and external datasets, yielding AUCs of 0.702 (95% CI: 0.547–0.858, p = 0.011) and 0.668 (95% CI: 0.500–0.838, p = 0.043), respectively. Conclusions: These results demonstrate that the selected nine radiomics features effectively differentiate malignant thyroid nodules. Overall, the radiomics model shows potential as a valuable predictive tool for thyroid cancer in patients with TIs, supporting improved preoperative decision-making. Full article
(This article belongs to the Special Issue Bridging the Gap: Integrating AI into Clinical Practice for Oncological PET/CT Imaging)
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16 pages, 2726 KiB  
Article
The Challenge of External Generalisability: Insights from the Bicentric Validation of a [68Ga]Ga-PSMA-11 PET Based Radiomics Signature for Primary Prostate Cancer Characterisation Using Histopathology as Reference
by Samuele Ghezzo, Praveen Gurunath Bharathi, Heying Duan, Paola Mapelli, Philipp Sorgo, Guido Alejandro Davidzon, Carolina Bezzi, Benjamin Inbeh Chung, Ana Maria Samanes Gajate, Alan Eih Chih Thong, Tommaso Russo, Giorgio Brembilla, Andreas Markus Loening, Pejman Ghanouni, Anna Grattagliano, Alberto Briganti, Francesco De Cobelli, Geoffrey Sonn, Arturo Chiti, Andrei Iagaru, Farshad Moradi and Maria Picchioadd Show full author list remove Hide full author list
Cancers 2024, 16(23), 4103; https://doi.org/10.3390/cancers16234103 - 7 Dec 2024
Viewed by 974
Abstract
Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to [...] Read more.
Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to validate a radiomics signature in a larger internal cohort and in an external cohort from a separate centre. Methods: One hundred and twenty-seven PCa patients were retrospectively enrolled across two independent hospitals. The first centre (IRCCS San Raffaele Scientific Institute, Centre 1) contributed 62 [68Ga]Ga-PSMA-11 PET scans, 20 patients classified as low-grade (ISUP grade < 4), and 42 as high-grade (ISUP grade ≥ 4). The second centre (Stanford University Hospital, Centre 2) provided 65 [68Ga]Ga-PSMA-11 PET scans, and 49 low-grade and 16 high-grade patients. A radiomics model previously generated in Centre 1 was tested on the two cohorts separately and afterward on the entire dataset. Then, we evaluated whether the radiomics features selected in the previous investigation could generalise to new data. Several machine learning (ML) models underwent training and testing using 100-fold Monte Carlo cross-validation, independently at both Centre 1 and Centre 2, with a 70–30% train–test split. Additionally, models were trained in one centre and tested in the other, and vice versa. Furthermore, data from both centres were combined for training and testing using Monte Carlo cross-validation. Finally, a new radiomics signature built on this bicentric dataset was proposed. Several performance metrics were computed. Results: The previously generated radiomics signature resulted in an area under the receiver operating characteristic curve (AUC) of 80.4% when tested on Centre 1, while it generalised poorly to Centre 2, where it reached an AUC of 62.7%. When the whole cohort was considered, AUC was 72.5%. Similarly, new ML models trained on the previously selected features yielded, at best, an AUC of 80.9% for Centre 1 and performed at chance for Centre 2 (AUC of 49.3%). A new signature built on this bicentric dataset reached, at best, an average AUC of 91.4% in the test set. Conclusions: The satisfying performance of radiomics models when used in the original development settings, paired with the poor performance otherwise observed, emphasises the need to consider centre-specific factors and dataset characteristics when developing radiomics models. Combining radiomics datasets is a viable strategy to reduce such centre-specific biases, but external validation is still needed. Full article
(This article belongs to the Special Issue Bridging the Gap: Integrating AI into Clinical Practice for Oncological PET/CT Imaging)
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Other

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24 pages, 3097 KiB  
Systematic Review
A Systematic Review of the Applications of Deep Learning for the Interpretation of Positron Emission Tomography Images of Patients with Lymphoma
by Theofilos Kanavos, Effrosyni Birbas and Theodoros P. Zanos
Cancers 2025, 17(1), 69; https://doi.org/10.3390/cancers17010069 - 29 Dec 2024
Viewed by 804
Abstract
Background: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as a reliable resource for the analysis of medical images. In this context, we systematically reviewed the applications of deep learning (DL) [...] Read more.
Background: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as a reliable resource for the analysis of medical images. In this context, we systematically reviewed the applications of deep learning (DL) for the interpretation of lymphoma PET images. Methods: We searched PubMed until 11 September 2024 for studies developing DL models for the evaluation of PET images of patients with lymphoma. The risk of bias and applicability concerns were assessed using the prediction model risk of bias assessment tool (PROBAST). The articles included were categorized and presented based on the task performed by the proposed models. Our study was registered with the international prospective register of systematic reviews, PROSPERO, as CRD42024600026. Results: From 71 papers initially retrieved, 21 studies with a total of 9402 participants were ultimately included in our review. The proposed models achieved a promising performance in diverse medical tasks, namely, the detection and histological classification of lesions, the differential diagnosis of lymphoma from other conditions, the quantification of metabolic tumor volume, and the prediction of treatment response and survival with areas under the curve, F1-scores, and R2 values of up to 0.963, 87.49%, and 0.94, respectively. Discussion: The primary limitations of several studies were the small number of participants and the absence of external validation. In conclusion, the interpretation of lymphoma PET images can reliably be aided by DL models, which are not designed to replace physicians but to assist them in managing large volumes of scans through rapid and accurate calculations, alleviate their workload, and provide them with decision support tools for precise care and improved outcomes. Full article
(This article belongs to the Special Issue Bridging the Gap: Integrating AI into Clinical Practice for Oncological PET/CT Imaging)
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