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Diagnostics
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Role of Artificial Intelligence from the Diagnosis to the Treatment...
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Role of Artificial Intelligence from the Diagnosis to the Treatment of Renal Cancer: Imaging, Histology and Beyond

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 5037

Special Issue Editors

Dr. Enrico Checcucci

E-Mail Website
Guest Editor
Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142, km 3, 95, 10060 Candiolo, Italy
Interests: urologic oncology; prostate cancer; urotechnology
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Amparore

E-Mail Website
Guest Editor
Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy
Interests: prostate cancer; robotics; laparoscopy; new technology; 3D; uro-oncology

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has made considerable progress in the last decade and is the subject of much of the contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal Cell Carcinoma (RCC) has shown even more importance at different moments of patients’ history, from diagnosis to intraoperative settings. In fact, approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. AI applications can be found in any aspect of RCC management, including diagnostics, perioperative care, pathology, and follow-up.

In this Special Issue, we invite authors to submit papers (clinical studies or reviews) to investigate the different aspects of AI for RCC, from imaging to histology and beyond, including experiences in pathology, radiology or urological interventions. 

Dr. Enrico Checcucci
Dr. Daniele Amparore
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. Diagnostics 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 2600 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.

Published Papers (3 papers)

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Review

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11 pages, 1930 KiB  
Review
The Use of Radiomic Tools in Renal Mass Characterization
by Beatriz Gutiérrez Hidalgo, Juan Gómez Rivas, Irene de la Parra, María Jesús Marugán, Álvaro Serrano, Juan Fco Hermida Gutiérrez, Jerónimo Barrera and Jesús Moreno-Sierra
Diagnostics 2023, 13(17), 2743; https://doi.org/10.3390/diagnostics13172743 - 24 Aug 2023
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Abstract
The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological [...] Read more.
The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence from the Diagnosis to the Treatment of Renal Cancer: Imaging, Histology and Beyond)
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25 pages, 2078 KiB  
Review
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives
by Alfredo Distante, Laura Marandino, Riccardo Bertolo, Alexandre Ingels, Nicola Pavan, Angela Pecoraro, Michele Marchioni, Umberto Carbonara, Selcuk Erdem, Daniele Amparore, Riccardo Campi, Eduard Roussel, Anna Caliò, Zhenjie Wu, Carlotta Palumbo, Leonardo D. Borregales, Peter Mulders and Constantijn H. J. Muselaers
Diagnostics 2023, 13(13), 2294; https://doi.org/10.3390/diagnostics13132294 - 6 Jul 2023
Cited by 1 | Viewed by 1974
Abstract
Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of [...] Read more.
Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems’ outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence from the Diagnosis to the Treatment of Renal Cancer: Imaging, Histology and Beyond)
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Other

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10 pages, 2555 KiB  
Case Report
Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications
by Michele Di Dio, Simona Barbuto, Claudio Bisegna, Andrea Bellin, Mario Boccia, Daniele Amparore, Paolo Verri, Giovanni Busacca, Michele Sica, Sabrina De Cillis, Federico Piramide, Vincenzo Zaccone, Alberto Piana, Stefano Alba, Gabriele Volpi, Cristian Fiori, Francesco Porpiglia and Enrico Checcucci
Diagnostics 2023, 13(14), 2320; https://doi.org/10.3390/diagnostics13142320 - 10 Jul 2023
Cited by 3 | Viewed by 1440
Abstract
Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI [...] Read more.
Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models’ reconstruction. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence from the Diagnosis to the Treatment of Renal Cancer: Imaging, Histology and Beyond)
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