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Advances in Medical Imaging and Radiation Therapy
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Advances in Medical Imaging and Radiation Therapy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 5359

Special Issue Editors

Dr. Marta Cremonesi

E-Mail Website
Guest Editor
Radiation Research Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
Interests: medical physics
Dr. Daniele Pistone

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania "Luigi Vanvitelli", Viale Abramo Lincoln 5, 81100 Caserta, CE, Italy
Interests: internal dosimetry; radionuclide therapies; BNCT; nuclear medicine; Monte Carlo simulation

Special Issue Information

Dear Colleagues,

Medical imaging has become irreplaceable in healthcare, enabling advanced non-invasive diagnoses and providing essential data for planning radiotherapeutic treatments, in turn becoming crucial in addressing tumor-related pathologies and serving as a powerful alternative or adjunct to surgery, chemotherapy, and other pharmacological treatments.

This Special Issue will offer an overview of recent advancements in imaging techniques, focusing on innovative approaches in nuclear medicine imaging, namely, PET and SPECT tomographies, and their complementarity with morphological CT and NMR.

Regarding radiation therapies, the papers published in this Special Issue will explore a comprehensive range of modalities, providing the state of the art from conventional external beam radiotherapies to molecular radiotherapies, including more innovative approaches like hadrontherapy, FLASH therapy, and recently revitalized techniques, such as Boron Neutron Capture Therapy (BNCT). Collateral aspects, such as radiobiological analyses and emerging computing technologies, e.g., artificial intelligence, contributing both to imaging and treatment planning, will also be analyzed.

This Special Issue could serve as a valuable resource for researchers, clinicians, and policymakers, aiming to exploit the full potential of these technologies for ever-increasing personalized and accurate treatments, ultimately enhancing patient care and clinical outcomes.

Dr. Marta Cremonesi
Dr. Daniele Pistone
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • medical imaging
  • radiation therapies
  • functional tomography, PET, SPECT
  • morphological tomography, CT, NMR
  • external beam radiotherapies, hadrotherapy, protontherapy, carbon-ion therapy, FLASH therapy
  • nuclear medicine, molecular radiotherapy, radionuclide therapy, Boron Neutron Capture Therapy (BNCT)
  • computational technologies, artificial intelligence, imaging diagnostics, radiobiology, segmentations, treatment planning, quantitative imaging

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Published Papers (4 papers)

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Research

21 pages, 2998 KiB  
Article
In-Depth Exploration of Advanced Settings in Personalized Automation of Radiotherapy Treatment Planning Using Reference Datasets
by Giulia Paolani, Miriam Santoro, Silvia Strolin, Alessio Giuseppe Morganti and Lidia Strigari
Appl. Sci. 2025, 15(3), 1071; https://doi.org/10.3390/app15031071 - 22 Jan 2025
Viewed by 691
Abstract
Introduction: The personalized planning engine in Pinnacle Evolution (version 16.4.3) for automated treatment planning incorporates the feasibility of DVH using several advanced optimization parameters that are generally empirically determined. Materials and Methods: Using the head and neck (HNCa) and prostate cancer (PCa) cases [...] Read more.
Introduction: The personalized planning engine in Pinnacle Evolution (version 16.4.3) for automated treatment planning incorporates the feasibility of DVH using several advanced optimization parameters that are generally empirically determined. Materials and Methods: Using the head and neck (HNCa) and prostate cancer (PCa) cases available from the AAPM TG-244 and the VMAT technique using an Elekta Versa HD LINAC, the role of the advanced optimization parameters has been investigated after identifying clinical goals. Plan complexity indexes were calculated using LINAC WATCH software v. 3.6 (Qualiformed, La Roche-sur-Yon, FRA) and correlated to dose distributions and dosimetric evaluations. Moreover, the plan deliverability was assessed using gamma (γ)-index analysis. Results: One hundred sixty plans were optimized (eighty per district). Out of the calculated ones, 88% and 100% were deliverable for HNCa and PCa, respectively, and 68% resulted in a γ-index ≥ 95% for both districts. Conclusions: Ad hoc measurements allowed us to identify a robust subset of acceptable input parameters based on γ-index passing rate criteria. Our approach identified advanced parameters to exploit the capability of the personalized planning engine of Pinnacle Evolution to be incorporated into the planning templates for HNCa and PCa radiotherapy planning. Full article
(This article belongs to the Special Issue Advances in Medical Imaging and Radiation Therapy)
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16 pages, 2295 KiB  
Article
Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
by Nektarios Georgiou, Pavlos Kolias and Ioanna Chouvarda
Appl. Sci. 2024, 14(22), 10731; https://doi.org/10.3390/app142210731 - 20 Nov 2024
Viewed by 1414
Abstract
The aim of this study was to explore the application of computational models for the analysis of histopathological images in the context of colon cancer. A comprehensive dataset of colon cancer images annotated into eight distinct categories based on their representation of cancerous [...] Read more.
The aim of this study was to explore the application of computational models for the analysis of histopathological images in the context of colon cancer. A comprehensive dataset of colon cancer images annotated into eight distinct categories based on their representation of cancerous cell portions was used. The primary objective was to employ various image classification algorithms to assess their efficacy in the context of cancer classification. Additionally, this study investigated the use of feature extraction techniques to derive meaningful data from the images, contributing to a more nuanced understanding of cancerous tissues, comparing the performance of different image classification algorithms in the context of colon cancer image analysis. The findings of this research suggested that XGboost provides the highest accuracy (89.79%) and could contribute to the growing body of knowledge in computational pathology. Other algorithms, such as the random forest, SVM, and CNN, also provided satisfactory results, offering insights into the effectiveness of image classification algorithms in distinguishing between different categories of cancerous cells. This work holds implications for the development of more accurate and efficient tools, underscoring the potential of computational models in enhancing the analysis of histopathological images and improving diagnostic capabilities in cancer research. Full article
(This article belongs to the Special Issue Advances in Medical Imaging and Radiation Therapy)
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12 pages, 1721 KiB  
Article
Image Quality and Information Parameters of Electronic Portal Imaging Devices
by Marios K. Tzomakas, Vasiliki Peppa, Antigoni Alexiou, Georgios Karakatsanis, Anastasios Episkopakis, Christos Michail, Ioannis Valais, George Fountos, Nektarios Kalyvas and Ioannis S. Kandarakis
Appl. Sci. 2024, 14(22), 10260; https://doi.org/10.3390/app142210260 - 7 Nov 2024
Cited by 1 | Viewed by 1331
Abstract
In this study, the imaging performance of electronic portal imaging devices (EPIDs) is evaluated, comparing measurements collected from EPID images captured at 115 cm, with a field size of 15 × 15 cm2, monitor units (MUs) in the range of 2 [...] Read more.
In this study, the imaging performance of electronic portal imaging devices (EPIDs) is evaluated, comparing measurements collected from EPID images captured at 115 cm, with a field size of 15 × 15 cm2, monitor units (MUs) in the range of 2 MU-100 MU and dose rates (DRs) of 200 MU/min, 400 MU/min and 600 MU/min, using a 6 MV LINAC system and the QC-3V image quality phantom. The analysis includes the normalized contrast transfer function (CTFnorm), the noise power spectrum (NPS) and the information capacity (IC), as well as the signal-to-noise frequency response (SNFR), which can be used as a comprehensive quality index. The results of our study are compared with previously published data captured at 100 cm under similar exposure conditions. They show similar CTF curves with different source-to-phantom distances, with the lowest values observed at specific MU and DR combinations. Moreover, NPS graphs are found to decrease with respect to spatial frequency. SNFR values also display a reduction with increasing spatial frequency. In addition, irradiation with the phantom placed closer to the EPID, 115 cm from the LINAC, yields better SNFR and IC performance characteristics, indicating better delineation of the organs closer to the EPID. The testing of EPID performance may potentially benefit from our results, which may lead to improvements in the quality of radiotherapy treatments. Full article
(This article belongs to the Special Issue Advances in Medical Imaging and Radiation Therapy)
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10 pages, 1972 KiB  
Article
The Role of Ultrasound-Based Monitoring of Bladder Volume in Patients with Prostate Cancer during CyberKnife Stereotactic Radiosurgery
by Dawid Bodusz, Joanna Brajczewska-Bello, Tomasz Rutkowski, Zofia Kołosza, Alexander Jorge Cortez, Magdalena Szymala-Cortez, Wojciech Leszczyński and Jerzy Wydmański
Appl. Sci. 2024, 14(19), 9091; https://doi.org/10.3390/app14199091 - 8 Oct 2024
Viewed by 1286
Abstract
(1) Background: For patients irradiated due to prostate cancer, monitoring of bladder volume (BV) is crucial for accuracy of radiation delivery and minimizing exposure to surrounding healthy tissues. Due to the limited imaging capabilities of the CyberKnife system, ultrasound imaging plays a vital [...] Read more.
(1) Background: For patients irradiated due to prostate cancer, monitoring of bladder volume (BV) is crucial for accuracy of radiation delivery and minimizing exposure to surrounding healthy tissues. Due to the limited imaging capabilities of the CyberKnife system, ultrasound imaging plays a vital role in the monitoring of bladder filling. (2) Methods: A study was carried out in 142 prostate cancer patients treated with the CyberKnife system. Bladder ultrasound (US) was performed before each RTH session to assess real BV (rBV). The double US assessment of rBV was performed by two independent operators or a single operator during 177 and 495 RTH sessions, respectively, giving, in total, 1344 BV assessments. (3) Results: The mean BV in the first and second US assessment was 214.7 mL and 218.1 mL, respectively, while the mean planning bladder volume (pBV) was 340.8 mL. A pBV was significantly higher than an rBV. The mean difference between the US and CT assessment of BV was the smallest in the 100–349 mL group and the largest in the group above 349 mL. The Passing–Bablok regression results confirmed the reliability of the ultrasound (US) measurements. (4) Conclusions: The introduction of US-based BV assessment in patients irradiated due to prostate cancer using CyberKnife seems to be a crucial element in controlling the reproducibility of the treatment plan and should be a standard procedure. The pBV should be within the range of 200–300 mL. The US examination prior to CT scanning for planning is recommended to ensure the optimal range of BV for CT. Full article
(This article belongs to the Special Issue Advances in Medical Imaging and Radiation Therapy)
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