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Developments in Artificial Intelligence and Advanced Medical Imaging in Cancers

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 9868

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Prof. Dr. Dania Cioni

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Academic Radiology, Department of Surgical, Medical, Molecular Pathogy and Emergency Medicine, University of Pisa, 56126 Pisa, Italy
Interests: magnetic resonance imaging; ultrasound; computed tomography; oncology; biomarkers; hepatocellular carcinoma; liver radiomics; artificial intelligence; machine learning; deep learning
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Dear Colleagues,

Medical imaging plays a pivotal role in many steps of oncologic patient management, from diagnosis to follow-up. The different domains of radiology oncologic imaging are the most suited to the implementation of new software and hardware technologies. In recent decades, several potential applications of artificial intelligence have been investigated, demonstrating remarkable effects in the diagnosis, prognosis, and assessment of response to therapy. The increasing number of AI-based software approved for clinical practice demonstrates the need for radiologists to be fully aware of the opportunities and challenges presented by these new technologies. Similarly, the increasing availability of new hardware technologies such as photon-counting CT, ultra-high-field MRI or ultra-high-frequency US, is rapidly transforming the imaging landscape. The purpose of this Special Issue is to collect papers addressing the latest advances in oncology medical imaging, both in terms of the impending integration of AI into the radiological workflow and the new hardware technologies with their outstanding diagnostic capabilities.

Prof. Dr. Dania Cioni
Guest Editor

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Keywords

oncologic imaging
artificial intelligence
machine learning
deep learning
radiomics
photon-counting computed tomography
spectral computed tomography
ultra-high-field magnetic resonance imaging
ultra-high-frequency ultrasound

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Published Papers (4 papers)

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16 pages, 2960 KiB

Open AccessArticle

Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames

by Muhammad Awais, Mais Al Taie, Caleb S. O’Connor, Austin H. Castelo, Belkacem Acidi, Hop S. Tran Cao and Kristy K. Brock

Cancers 2024, 16(21), 3674; https://doi.org/10.3390/cancers16213674 - 30 Oct 2024

Cited by 1 | Viewed by 1462

Abstract

Background/Objectives: In the field of surgical medicine, the planning and execution of liver resection procedures present formidable challenges, primarily attributable to the intricate and highly individualized nature of liver vascular anatomy. In the current surgical milieu, intraoperative ultrasonography (IOUS) has become indispensable; however, traditional 2D ultrasound imaging’s interpretability is hindered by noise and speckle artifacts. Accurate identification of critical structures for preservation during hepatectomy requires advanced surgical skills. Methods: An AI-based model that can help detect and recognize vessels including the inferior vena cava (IVC); the right (RHV), middle (MHV), and left (LVH) hepatic veins; the portal vein (PV) and its major first and second order branches the left portal vein (LPV), right portal vein (RPV), and right anterior (RAPV) and posterior (RPPV) portal veins, for real-time IOUS navigation can be of immense value in liver surgery. This research aims to advance the capabilities of IOUS-guided interventions by applying an innovative AI-based approach named the “2D-weigthed U-Net model” for the segmentation of multiple blood vessels in real-time IOUS video frames. Results: Our proposed deep learning (DL) model achieved a mean Dice score of 0.92 for IVC, 0.90 for RHV, 0.89 for MHV, 0.86 for LHV, 0.95 for PV, 0.93 for LPV, 0.84 for RPV, 0.85 for RAPV, and 0.96 for RPPV. Conclusion: In the future, this research will be extended for real-time multi-label segmentation of extended vasculature in the liver, followed by the translation of our model into the surgical suite. Full article

(This article belongs to the Special Issue Developments in Artificial Intelligence and Advanced Medical Imaging in Cancers)

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17 pages, 3526 KiB

Open AccessArticle

Outcome Prediction Using Multi-Modal Information: Integrating Large Language Model-Extracted Clinical Information and Image Analysis

by Di Sun, Lubomir Hadjiiski, John Gormley, Heang-Ping Chan, Elaine Caoili, Richard Cohan, Ajjai Alva, Grace Bruno, Rada Mihalcea, Chuan Zhou and Vikas Gulani

Cancers 2024, 16(13), 2402; https://doi.org/10.3390/cancers16132402 - 29 Jun 2024

Cited by 5 | Viewed by 2659

Abstract

Survival prediction post-cystectomy is essential for the follow-up care of bladder cancer patients. This study aimed to evaluate artificial intelligence (AI)-large language models (LLMs) for extracting clinical information and improving image analysis, with an initial application involving predicting five-year survival rates of patients after radical cystectomy for bladder cancer. Data were retrospectively collected from medical records and CT urograms (CTUs) of bladder cancer patients between 2001 and 2020. Of 781 patients, 163 underwent chemotherapy, had pre- and post-chemotherapy CTUs, underwent radical cystectomy, and had an available post-surgery five-year survival follow-up. Five AI-LLMs (Dolly-v2, Vicuna-13b, Llama-2.0-13b, GPT-3.5, and GPT-4.0) were used to extract clinical descriptors from each patient’s medical records. As a reference standard, clinical descriptors were also extracted manually. Radiomics and deep learning descriptors were extracted from CTU images. The developed multi-modal predictive model, CRD, was based on the clinical (C), radiomics (R), and deep learning (D) descriptors. The LLM retrieval accuracy was assessed. The performances of the survival predictive models were evaluated using AUC and Kaplan–Meier analysis. For the 163 patients (mean age 64 ± 9 years; M:F 131:32), the LLMs achieved extraction accuracies of 74%~87% (Dolly), 76%~83% (Vicuna), 82%~93% (Llama), 85%~91% (GPT-3.5), and 94%~97% (GPT-4.0). For a test dataset of 64 patients, the CRD model achieved AUCs of 0.89 ± 0.04 (manually extracted information), 0.87 ± 0.05 (Dolly), 0.83 ± 0.06~0.84 ± 0.05 (Vicuna), 0.81 ± 0.06~0.86 ± 0.05 (Llama), 0.85 ± 0.05~0.88 ± 0.05 (GPT-3.5), and 0.87 ± 0.05~0.88 ± 0.05 (GPT-4.0). This study demonstrates the use of LLM model-extracted clinical information, in conjunction with imaging analysis, to improve the prediction of clinical outcomes, with bladder cancer as an initial example. Full article

(This article belongs to the Special Issue Developments in Artificial Intelligence and Advanced Medical Imaging in Cancers)

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13 pages, 2066 KiB

Open AccessArticle

Abdominal Visceral-to-Subcutaneous Fat Volume Ratio Predicts Survival and Response to First-Line Palliative Chemotherapy in Patients with Advanced Gastric Cancer

by Giacomo Aringhieri, Gianfranco Di Salle, Silvia Catanese, Caterina Vivaldi, Francesca Salani, Saverio Vitali, Miriam Caccese, Enrico Vasile, Virginia Genovesi, Lorenzo Fornaro, Rachele Tintori, Francesco Balducci, Carla Cappelli, Dania Cioni, Gianluca Masi and Emanuele Neri

Cancers 2023, 15(22), 5391; https://doi.org/10.3390/cancers15225391 - 13 Nov 2023

Cited by 5 | Viewed by 1719

Abstract

Prognosis in advanced gastric cancer (aGC) is predicted by clinical factors, such as stage, performance status, metastasis location, and the neutrophil-to-lymphocyte ratio. However, the role of body composition and sarcopenia in aGC survival remains debated. This study aimed to evaluate how abdominal visceral and subcutaneous fat volumes, psoas muscle volume, and the visceral-to-subcutaneous (VF/SF) volume ratio impact overall survival (OS) and progression-free survival (PFS) in aGC patients receiving first-line palliative chemotherapy. We retrospectively examined CT scans of 65 aGC patients, quantifying body composition parameters (BCPs) in 2D and 3D. Normalized 3D BCP volumes were determined, and the VF/SF ratio was computed. Survival outcomes were analyzed using the Cox Proportional Hazard model between the upper and lower halves of the distribution. Additionally, response to first-line chemotherapy was compared using the χ² test. Patients with a higher VF/SF ratio (N = 33) exhibited significantly poorer OS (p = 0.02) and PFS (p < 0.005) and had a less favorable response to first-line chemotherapy (p = 0.033), with a lower Disease Control Rate (p = 0.016). Notably, absolute BCP measures and sarcopenia did not predict survival. In conclusion, radiologically assessed VF/SF volume ratio emerged as a robust and independent predictor of both survival and treatment response in aGC patients. Full article

(This article belongs to the Special Issue Developments in Artificial Intelligence and Advanced Medical Imaging in Cancers)

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31 pages, 2367 KiB

Open AccessSystematic Review

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review

by Wilson Ong, Aric Lee, Wei Chuan Tan, Kuan Ting Dominic Fong, Daoyong David Lai, Yi Liang Tan, Xi Zhen Low, Shuliang Ge, Andrew Makmur, Shao Jin Ong, Yong Han Ting, Jiong Hao Tan, Naresh Kumar and James Thomas Patrick Decourcy Hallinan

Cancers 2024, 16(17), 2988; https://doi.org/10.3390/cancers16172988 - 28 Aug 2024

Cited by 3 | Viewed by 3150

Abstract

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI’s potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice. Full article

(This article belongs to the Special Issue Developments in Artificial Intelligence and Advanced Medical Imaging in Cancers)

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