Feature Papers in Section "Methods and Technologies Development"

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 5543

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Guest Editor
Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sant’Andrea University Hospital, University of Rome Sapienza, Via di Grottarossa 1035, 00189 Rome, Italy
Interests: imaging; oncology; CT; MRI; artificial intelligence; radiomics; response to therapy
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Department of Surgical and Medical Sciences and Translational Medicine, Radiology Unit, Sant’Andrea University Hospital, University of Rome Sapienza, Via di Grottarossa 1035, 00189 Rome, Italy
Interests: oncologic imaging; quantitative imaging; artificial intelligence application; MRI; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medicine and in particular Medical Imaging have a central role in the screening, diagnosis, treatment and follow-up of cancer. In such scenarios, new technologies play a pivotal role in the improvement of early detection, precise characterization and therapeutic strategies for patients affected by cancer. Nowadays, new methods are emerging that benefit from innovative technological changes. A striking example is the advent in medical research of Artificial Intelligence that has many wide applications, with the aim of improving medical processes (e.g., imaging acquisitions, big data integration, response to therapy prediction, etc.). Due to the rapid development of new technologies, it is important to share the latest innovations in this dynamic field and to facilitate the dissemination of knowledge to improve cancer diagnosis and treatment on a large scale.

We are delighted to present this Special Issue of Cancers titled "Feature Paper in Section "Methods and Technologies Development." This Special Issue is dedicated to the intersection of new technological strategies and cancer diagnostic methods, marking a milestone in the evolution of diagnostic and therapeutic strategies.

We hope that contributions in this Special Issue will reflect the collaborative work of multidisciplinary experts in the fields of Medicine, Data Science and Technology.

Dr. Damiano Caruso
Guest Editor

Marta Zerunian
Guest Editor Assistant

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. 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

  • innovative diagnostic technology
  • artificial intelligence
  • cancer diagnosis
  • personalized treatment
  • diagnostic and therapeutic strategies

Published Papers (6 papers)

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Research

13 pages, 1824 KiB  
Article
Prospective Assessment of Fluorine-18-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography (FDG-PET/CT) for Early Identification of Checkpoint-Inhibitor-Induced Pseudoprogression
by Sif Homburg, Charlotte Birk Christensen, Magnus Pedersen, Simon Grund Sørensen, Marco Donia, Inge Marie Svane, Helle Westergren Hendel and Eva Ellebaek
Cancers 2024, 16(5), 964; https://doi.org/10.3390/cancers16050964 - 27 Feb 2024
Viewed by 657
Abstract
The activity of immune checkpoint inhibitors (ICIs) in patients with metastatic melanoma is often monitored using fluorine-18-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) scans. However, distinguishing disease progression (PD) from pseudoprogression (PsPD), where increased FDG uptake might reflect immune cell activity rather than tumor growth, [...] Read more.
The activity of immune checkpoint inhibitors (ICIs) in patients with metastatic melanoma is often monitored using fluorine-18-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) scans. However, distinguishing disease progression (PD) from pseudoprogression (PsPD), where increased FDG uptake might reflect immune cell activity rather than tumor growth, remains a challenge. This prospective study compared the efficacy of dual-time point (DTP) FDG-PET/CT with modified response criteria (PERCIMT) in differentiating PsPD from PD. From July 2017–January 2021, 41 patients suspected to have PsPD on an evaluation scan were prospectively included (29 evaluable). A subsequent DTP FDG-PET/CT scan was conducted within 14 days, followed by a confirmatory FDG-PET/CT scan. Additionally, PERCIMT were applied. DTP FDG-PET/CT identified 24% with PsPD and 76% with PD. Applying PERCIMT criteria, 69% showed PsPD, while 31% had PD. On follow-up, 10 patients (34%) demonstrated confirmed PsPD, while 19 (66%) exhibited PD. The sensitivity and specificity of DTP FDG-PET/CT were 20% and 74%, respectively, and for PERCIMT this was 80% and 37%, respectively. Our findings suggest limited efficacy of DTP FDG-PET/CT in distinguishing PsPD from PD in ICI-treated patients with metastatic melanoma. The use of PERCIMT could complement clinical assessment and be incorporated in multidisciplinary team conferences for enhanced decision-making. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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23 pages, 5097 KiB  
Article
Transduction Efficiency of Zika Virus E Protein Pseudotyped HIV-1gfp and Its Oncolytic Activity Tested in Primary Glioblastoma Cell Cultures
by Jan Patrick Formanski, Hai Dang Ngo, Vivien Grunwald, Celine Pöhlking, Jana Sue Jonas, Dominik Wohlers, Birco Schwalbe and Michael Schreiber
Cancers 2024, 16(4), 814; https://doi.org/10.3390/cancers16040814 - 17 Feb 2024
Viewed by 983
Abstract
The development of new tools against glioblastoma multiforme (GBM), the most aggressive and common cancer originating in the brain, remains of utmost importance. Lentiviral vectors (LVs) are among the tools of future concepts, and pseudotyping offers the possibility of tailoring LVs to efficiently [...] Read more.
The development of new tools against glioblastoma multiforme (GBM), the most aggressive and common cancer originating in the brain, remains of utmost importance. Lentiviral vectors (LVs) are among the tools of future concepts, and pseudotyping offers the possibility of tailoring LVs to efficiently transduce and inactivate GBM tumor cells. Zika virus (ZIKV) has a specificity for GBM cells, leaving healthy brain cells unharmed, which makes it a prime candidate for the development of LVs with a ZIKV coat. Here, primary GBM cell cultures were transduced with different LVs encased with ZIKV envelope variants. LVs were generated by using the pNLgfpAM plasmid, which produces the lentiviral, HIV-1-based, core particle with GFP (green fluorescent protein) as a reporter (HIVgfp). Using five different GBM primary cell cultures and three laboratory-adapted GBM cell lines, we showed that ZIKV/HIVgfp achieved a 4–6 times higher transduction efficiency compared to the commonly used VSV/HIVgfp. Transduced GBM cell cultures were monitored over a period of 9 days to identify GFP+ cells to study the oncolytic effect due to ZIKV/HIVgfp entry. Tests of GBM tumor specificity by transduction of GBM tumor and normal brain cells showed a high specificity for GBM cells. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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13 pages, 4526 KiB  
Article
Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer
by Marta Zerunian, Ilaria Nacci, Damiano Caruso, Michela Polici, Benedetta Masci, Domenico De Santis, Paolo Mercantini, Giulia Arrivi, Federica Mazzuca, Pasquale Paolantonio, Emanuela Pilozzi, Andrea Vecchione, Mariarita Tarallo, Enrico Fiori, Elsa Iannicelli and Andrea Laghi
Cancers 2024, 16(3), 660; https://doi.org/10.3390/cancers16030660 - 4 Feb 2024
Viewed by 841
Abstract
The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 [...] Read more.
The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0–2 points) and high likelihood (HLM: 3–7 points) of malignancy. The T-test and the Mann–Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN’s status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69–0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52–0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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11 pages, 2923 KiB  
Article
Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma
by Tom Perik, Natália Alves, John J. Hermans and Henkjan Huisman
Cancers 2024, 16(3), 577; https://doi.org/10.3390/cancers16030577 - 30 Jan 2024
Viewed by 733
Abstract
CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS). Methods: From January [...] Read more.
CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS). Methods: From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan–Meier curves. Differences between phenotypes were tested using the Mann–Whitney U test. Results: The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s, p < 0.001) and peak enhancement (70 HU vs. 47 HU, p < 0.001); the AUC was 0.86. The AI biomarker’s vascular phenotype significantly differed in OS (p < 0.01). Conclusions: The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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13 pages, 2674 KiB  
Article
RenseNet: A Deep Learning Network Incorporating Residual and Dense Blocks with Edge Conservative Module to Improve Small-Lesion Classification and Model Interpretation
by Hyunseok Seo, Seokjun Lee, Sojin Yun, Saebom Leem, Seohee So and Deok Hyun Han
Cancers 2024, 16(3), 570; https://doi.org/10.3390/cancers16030570 - 29 Jan 2024
Viewed by 652
Abstract
Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based [...] Read more.
Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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16 pages, 5413 KiB  
Article
Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration
by Zhengqiang Jiang, Ziba Gandomkar, Phuong Dung (Yun) Trieu, Seyedamir Tavakoli Taba, Melissa L. Barron, Peyman Obeidy and Sarah J. Lewis
Cancers 2024, 16(2), 322; https://doi.org/10.3390/cancers16020322 - 11 Jan 2024
Viewed by 1098
Abstract
This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and [...] Read more.
This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a ‘high-concordances subset’ with 99% agreement of cancer location and an ‘entire dataset’ with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists’ annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. Full article
(This article belongs to the Special Issue Feature Papers in Section "Methods and Technologies Development")
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