The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]

Conti, Vincenzo

Han, Changhee

Militello, Carmelo

Rundo, Leonardo

Zaccagna, Fulvio

Journal of Imaging

English

The Special Issue “Advanced Computational Methods for Oncological Image Analysis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...

Leonardo Rundo

Carmelo Militello

Vincenzo Conti

Fulvio Zaccagna

Changhee Han

Directory of Open Access Journals

Advanced Computational Methods for Oncological Image Analysis

Apollo (Cambridge)

Journal ofImagingEditorialAdvanced Computational Methods for OncologicalImage AnalysisLeonardo Rundo 1,2,* , Carmelo Militello 3,* , Vincenzo Conti 4 , Fulvio Zaccagna 5,6 and Changhee Han 7Citation: Rundo, L.; Militello, C.;Conti, V.; Zaccagna, F.; Han, C.Advanced Computational Methodsfor Oncological Image Analysis. J.Imaging 2021, 7, 237. https://doi.org/10.3390/jimaging7110237Received: 9 November 2021Accepted: 9 November 2021Published: 12 November 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK2 Department of Information and Electrical Engineering and Applied Mathematics (DIEM),University of Salerno, 84084 Fisciano, Italy3 Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR),90015 Cefalù, Italy4 Faculty of Engineering and Architecture, University of Enna KORE, 94100 Enna, Italy;vincenzo.conti@unikore.it5 Department of Biomedical and Neuromotor Sciences, University of Bologna, 40138 Bologna, Italy;fulvio.zaccagna@unibo.it6 IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit,40139 Bologna, Italy7 Saitama Prefectural University, Saitama 343-8540, Japan; mykallis13@gmail.com* Correspondence: lrundo@unisa.it (L.R.); carmelo.militello@ibfm.cnr.it (C.M.)The Special Issue “Advanced Computational Methods for Oncological Image Analy-sis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computationalmethods for oncological image analysis, ranging from radiogenomics to deep learning.Interesting review articles were also considered.Nowadays, the amount of heterogeneous biomedical data is constantly increasing, owingto the advances in image acquisition modalities and high-throughput technologies [1–3].In particular, this trend applies to oncological image analysis [4]. Cancer is the secondmost common cause of death worldwide and encompasses highly variable clinical andbiological scenarios. Some of the current clinical challenges are (i) early disease diagnosisand (ii) precision medicine, which allows for treatments targeted at specific clinical cases.The ultimate goal is to optimize the clinical workflow by combining accurate diagnosiswith the most suitable therapies [5].The automated analysis of these large-scale datasets creates new compelling challengesthat require advanced computational methods, ranging from classic machine learningtechniques [6,7] to deep learning [8,9].The developed reliable computer-assisted methods (i.e., artificial intelligence), to-gether with clinicians’ unique knowledge, can be used to properly handle typical issuesin evaluation/quantification procedures (i.e., operator dependence and time-consumingtasks) [10]. These technological advances can significantly improve result repeatability indisease diagnosis and act as a guide towards appropriate cancer care. Indeed, the needfor applying machine learning and computational intelligence techniques to effectivelyperform image processing operations —such as segmentation, co-registration, classification,and dimensionality reduction, and multi-omics data integration—has steadily increased.This Special Issue collects 13 papers related to oncological image analysis, including10 original contributions and 3 review articles.In the last few years, the role of medical image computing and quantification has beenremarkably growing. Several areas have benefited from these advances, including oncol-ogy, since the advancement of computational techniques provides a technological bridgebetween radiology and oncology. This aspect could significantly accelerate the adoptionof precision medicine. Regarding medical imaging focusing on traditional image analysisJ. Imaging 2021, 7, 237. https://doi.org/10.3390/jimaging7110237 https://www.mdpi.com/journal/jimagingJ. Imaging 2021, 7, 237 2 of 4tasks—such as registration, fusion, and segmentation—in recent years we have witnessedthe advances of model-based medical image processing for biomarker development [11].Among sex-related cancers, breast cancer for women and prostate cancer for men aremajor causes of disease and death.Concerning breast cancer, methods to predict its risk or to stratify women in differ-ent risk levels could help achieve early diagnosis and consequently, mortality reduction.Literature reviews are useful in providing a comprehensive vision of computer-assistedapproaches to support the clinical process, especially for young scientists [12,13]. In par-ticular, [14] reviews extraction methods of textural features from mammograms, wheremachine learning and deep learning algorithms are used to infer knowledge from thefeatures and assess breast cancer risk. The accurate diagnosis of breast cancer is verychallenging due to the increasing disease complexity, such as changes in treatment proce-dures and patient population samples. Improving the performance with suitable diagnosistechniques could lead to personalized care and treatment, thus reducing and controllingcancer recurrence [15].Even though magnetic resonance (MR) has a better capability to differentiate softtissues, mammography is the primary imaging modality used for the screening and earlydetection of breast cancer. The analysis of mammography images starts with detectingregions of interest around tumors. Those regions are then delimited through segmentationand classified as probably benign or malignant tumors. Meanwhile, the manual detectionand delimitation of masses in images is time consuming and error prone. Therefore,integrated computer-aided detection systems have been proposed to assist radiologists inthe process [16].Along with the well-known imaging modalities, such as MR, CT, PET, US, whichare now consolidated and used in clinical routine, recently new modalities have emergedthat exploit techniques initially born in non-clinical contexts, such as microwaves [17,18].When the aim is to reconstruct the dielectric/conductivity profile of the tissue underexamination, “quantitative” algorithms must be adopted. In these cases, the reconstructionsare basically optimized iteratively to consider the non-linearity. Among linear imagingmethods, commonly addressed as radar approaches, beam forming (BF) is probably themost popular in microwave breast imaging. Basically, it consists of time-shifting thesignals received over the measurement aperture to isolate signals scattered from (and henceto focus at) a particular synthetic focal point belonging to the imaged spatial area [17].Microwave-based tomography is a model-based imaging modality that approximatelyreconstructs the actual internal spatial distribution of a breast's dielectric properties over areconstruction model consisting of discrete elements. Breast tissue types are characterizedby their dielectric properties, so the complex permittivity profile could help distinguishdifferent tissue types [18].Prostate cancer is one of the most diagnosed cancers in men and can often cause bonemetastases. In this case, the most common imaging technique for screening, diagnosis,and the follow-up of disease evolution is bone scintigraphy, due to its high sensitivity andwidespread availability in nuclear medicine facilities. To date, the assessment of bone scansrelies solely on the interpretation of an expert physician who visually assesses the scan.This time-consuming task is also subjective, due to the lack of well-established criteriato identify bone metastases and quantify them using a straightforward and universallyaccepted procedure. The aim of the work in [19] was to provide the physician with a fast,precise, and reliable tool to quantify bone scans and evaluate disease progression/responseto treatment.Immunotherapy is one of the most significant breakthroughs in cancer treatment.Unfortunately, only a few patients respond positively to the treatment. Moreover, to date,no efficient biomarkers exist for discriminating patients eligible for this treatment in anearly stage. To help overcome these limitations, the development of tools for discriminatingbetween patients with high chances of response and those with disease progression isneeded [20].J. Imaging 2021, 7, 237 3 of 4Among tumors, brain lesions are one of the foremost reasons for the rise in mortalityamong children and adults. A brain tumor is a mass of tissue that propagates out of controlof the normal forces that regulate growth inside the brain [21]. The quantitative analysisof brain tumors provides valuable information for understanding tumor characteristicsand planning better treatment. The manual segmentation of brain tumors is a challengingand time-consuming task. The accurate segmentation of lesions requires multiple imagemodalities with varying contrasts. As a result, manual segmentation, which is arguablythe most accurate segmentation method, would be impractical for more extensive studies.Moreover, automated brain tumor classification on MRI is non-invasive, so that it avoidsbiopsy and makes the diagnosis process safer. The effort of the research communityto propose automatic brain tumor segmentation and classification methods has beentremendous. As a result, ample literature exists on segmentation using region growing,traditional machine learning and deep learning methods [22,23]. Similarly, a number oftasks have been successfully conducted in the area of brain tumor classification into theirrespective histological type.Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasoundsare currently emerging in clinical practice as a non-invasive technology for therapy. Indeed,the sound waves can increase the temperature inside the target solid tumors, leading to theapoptosis or necrosis of neoplastic tissues. The MR-guided focused ultrasound surgery(MRgFUS) technology represents a valid application of this ultrasound property, mainlyused in oncology and neurology [24]. Patient safety during MRgFUS treatments wasinvestigated because temperature increases during the treatment are not always accuratelydetected by MRI-based referenceless thermometry methods. For these reasons, in-depthstudies about these aspects are needed to monitor temperature and improve safety duringMRgFUS treatments.Deep learning approaches represent state-of-the-art techniques in many clinical sce-narios, allowing for excellent performance. In the clinical setting, the main problem derivesfrom their black-box approach (i.e., the nature of neural networks)—understanding andinterpreting their internal mechanisms are difficult. Moreover, they require a training phaseon large-scale datasets. These drawbacks undermine their immediate clinical feasibility.Apart from that, deep learning architectures, specifically convolutional neural networks(CNNs), are well-established in image analysis, processing, and representation. They canoptimize feature design tasks that are essential to automatically analyze different types ofmedical images [25–27]. Various approaches have been developed using CNN architectures,aiming to support the clinical routine, such as tumor segmentation [16], skin melanomaprediction [28], and the estimation of the immunotherapy treatment response [20].Acknowledgments: The guest editors, and all the editorial staff, would like to express their gratitudeto the authors, who have chosen to publish their articles in this Special Issue in the Journal of Imaging,as well as to the reviewers whose precious support in their evaluation of the manuscripts allowedus to select only high-quality contributions. We appreciate their demonstrated professionalism andeffort despite the COVID-19 pandemic.References1. Castiglioni, I.; Rundo, L.; Codari, M.; Di Leo, G.; Salvatore, C.; Interlenghi, M.; Gallivanone, F.; Cozzi, A.; D’Amico, N.C.;Sardanelli, F. AI Applications to Medical Images: From Machine Learning to Deep Learning. Phys. Med. 2021, 83, 9–24. [CrossRef]2. Rundo, L.; Militello, C.; Vitabile, S.; Russo, G.; Sala, E.; Gilardi, M.C. A Survey on Nature-Inspired Medical Image Analysis:A Step Further in Biomedical Data Integration. Fund. Inform. 2019, 171, 345–365. [CrossRef]3. Badr, E. Images in Space and Time. ACM Comput. Surv. 2021, 54, 345–365. [CrossRef]4. Bi, W.L.; Hosny, A.; Schabath, M.B.; Giger, M.L.; Birkbak, N.J.; Mehrtash, A.; Allison, T.; Arnaout, O.; Abbosh, C.; Dunn, I.F.; et al.Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications. CA Cancer J. Clin. 2019, 69, 127–157. [CrossRef][PubMed]5. Topol, E.J. High-Performance Medicine: The Convergence of Human and Artificial Intelligence. Nat. Med. 2019, 25, 44–56.[CrossRef]J. Imaging 2021, 7, 237 4 of 46. Conti, V.; Militello, C.; Rundo, L.; Vitabile, S. A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks. 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Advanced Computational Methods for Oncological Image Analysis.

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Advanced Computational Methods for Oncological Image Analysis.

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