Advances in Oncological Imaging

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 21848

Special Issue Editors


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Reparto di Radiologia, Ospedale Fatebenefratelli, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy
Interests: MRI; neuroimaging; CT; ultrasound; X-ray; lymphedema; magnetic resonance lymphography; magnetic resonance; brain imaging; brain mri; DECT; dual energy CT; lipedema; paranasal sinus imaing; lung cancer screening; low dose CT; COVID-19; neuromyelitis optica; radiography; AI

Special Issue Information

Dear Colleagues,

We are pleased to introduce a Special Issue focused on the latest imaging technologies and approaches for detecting, diagnosing, and monitoring various types of cancer. With cancer being one of the leading causes of death worldwide, there is an urgent need for novel and more effective imaging methods that can detect cancer at an early stage, accurately predict patient outcomes, and aid in personalized treatment planning. This Special Issue brings together a collection of original research articles, reviews, and perspectives that showcase cutting-edge imaging technologies and innovative approaches in cancer imaging, including molecular imaging, radiomics, artificial intelligence, and multimodal imaging. Our goal is to provide a comprehensive overview of the current state of cancer imaging and to promote the translation of these advances into clinical practice to improve patient outcomes.

Recent advancements in oncological imaging technologies have greatly improved the detection and treatment of malignancy.

Developments in MRI and CT equipment allowed significant improvement in image quality and reduction in acquisition times. Moreover, CT has also benefited from deep learning algorithms to reconstruct high-quality images from low-dose data, with a reduction of radiation exposure by up to 80% compared to standard CT scans.

Multimodal imaging technologies allow for a more comprehensive view of the patient’s cancer, which can improve diagnosis and treatment planning.

Artificial intelligence (AI) is also being increasingly used in oncological imaging to aid in the detection, diagnosis, and treatment of cancer. AI applications include automated tumor detection and segmentation to help radiologists in rapid and accurate tumor detection and characterization.

Radiomics feature extraction combined with AI predictive models aid cancer diagnosis, prognosis, and treatment response prediction to provide a customized approach and ultimately improve patient outcomes.

This Special Issue will highlight the novelties available in oncological imaging in the field of image acquisition and reconstruction technologies, as well as AI applications for the automatic detection, segmentation, and feature extraction of data, to achieve an increasingly personalized medicine.

Dr. Michaela Cellina
Dr. Filippo Pesapane
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. 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

  • oncological imaging
  • radiomics
  • deep learning
  • machine learning
  • image reconstruction
  • artificial intelligence
  • outcome prediction
  • oncological treatment planning
  • advanced imaging
  • personalized medicine

Published Papers (15 papers)

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Research

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13 pages, 2561 KiB  
Article
Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network
by Jiann-Shu Lee and Wen-Kai Wu
Cancers 2024, 16(7), 1362; https://doi.org/10.3390/cancers16071362 - 30 Mar 2024
Viewed by 604
Abstract
Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification model of breast [...] Read more.
Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification model of breast pathology images needs to have the ability to make a correct classification, even in facing image data with different characteristics. The existing convolutional neural network (CNN)-based models for the classification of breast tumor pathology images lack the requisite generalization capability to maintain high accuracy when confronted with pathology images of varied characteristics. Consequently, this study introduces a new classification model, STMLAN (Single-Task Meta Learning with Auxiliary Network), which integrates Meta Learning and an auxiliary network. Single-Task Meta Learning was proposed to endow the model with generalization ability, and the auxiliary network was used to enhance the feature characteristics of breast pathology images. The experimental results demonstrate that the STMLAN model proposed in this study improves accuracy by at least 1.85% in challenging multi-classification tasks compared to the existing methods. Furthermore, the Silhouette Score corresponding to the features learned by the model has increased by 31.85%, reflecting that the proposed model can learn more discriminative features, and the generalization ability of the overall model is also improved. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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10 pages, 644 KiB  
Article
Detection and Classification of Hysteroscopic Images Using Deep Learning
by Diego Raimondo, Antonio Raffone, Paolo Salucci, Ivano Raimondo, Giampiero Capobianco, Federico Andrea Galatolo, Mario Giovanni Cosimo Antonio Cimino, Antonio Travaglino, Manuela Maletta, Stefano Ferla, Agnese Virgilio, Daniele Neola, Paolo Casadio and Renato Seracchioli
Cancers 2024, 16(7), 1315; https://doi.org/10.3390/cancers16071315 - 28 Mar 2024
Viewed by 659
Abstract
Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings [...] Read more.
Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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12 pages, 1039 KiB  
Article
Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer
by Yu-Jen Fang, Chien-Wei Huang, Riya Karmakar, Arvind Mukundan, Yu-Ming Tsao, Kai-Yao Yang and Hsiang-Chen Wang
Cancers 2024, 16(3), 572; https://doi.org/10.3390/cancers16030572 - 29 Jan 2024
Cited by 1 | Viewed by 928
Abstract
Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, [...] Read more.
Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model’s performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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11 pages, 3473 KiB  
Article
Evaluation of Extra-Prostatic Extension on Deep Learning-Reconstructed High-Resolution Thin-Slice T2-Weighted Images in Patients with Prostate Cancer
by Mingyu Kim, Seung Ho Kim, Sujin Hong, Yeon Jung Kim, Hye Ri Kim and Joo Yeon Kim
Cancers 2024, 16(2), 413; https://doi.org/10.3390/cancers16020413 - 18 Jan 2024
Viewed by 749
Abstract
The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WIconv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WIHR, 2 mm), with and without [...] Read more.
The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WIconv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WIHR, 2 mm), with and without deep learning reconstruction (DLR) in patients with prostatic cancer (PCa). A total of 88 consecutive patients (28 EPE-positive and 60 negative) diagnosed with PCa via radical prostatectomy who had undergone 3T-MRI were included. Two independent reviewers performed a crossover review in three sessions, in which each reviewer recorded five-point confidence scores for the presence of EPE and image quality using a five-point Likert scale. Pathologic topographic maps served as the reference standard. For both reviewers, T2WIconv showed better diagnostic performance than T2WIHR with and without DLR (AUCs, in order, for reviewer 1, 0.883, 0.806, and 0.772, p = 0.0006; for reviewer 2, 0.803, 0.762, and 0.745, p = 0.022). The image quality was also the best in T2WIconv, followed by T2WIHR with DLR and T2WIHR without DLR for both reviewers (median, in order, 3, 4, and 5, p < 0.0001). In conclusion, T2WIconv was optimal in regard to image quality and diagnostic performance for the evaluation of EPE in patients with PCa. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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19 pages, 11660 KiB  
Article
Identification of Skin Lesions by Snapshot Hyperspectral Imaging
by Hung-Yi Huang, Hong-Thai Nguyen, Teng-Li Lin, Penchun Saenprasarn, Ping-Hung Liu and Hsiang-Chen Wang
Cancers 2024, 16(1), 217; https://doi.org/10.3390/cancers16010217 - 2 Jan 2024
Cited by 1 | Viewed by 1862
Abstract
This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 [...] Read more.
This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model’s robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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14 pages, 29798 KiB  
Article
AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images
by Yiqing Liu, Tiantian Zhen, Yuqiu Fu, Yizhi Wang, Yonghong He, Anjia Han and Huijuan Shi
Cancers 2024, 16(1), 167; https://doi.org/10.3390/cancers16010167 - 29 Dec 2023
Cited by 1 | Viewed by 1113
Abstract
Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting [...] Read more.
Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model’s predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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12 pages, 2688 KiB  
Article
Enhanced Precision and Safety in Thermal Ablation: O-Arm Cone Beam Computed Tomography with Magnetic Resonance Imaging Fusion for Spinal Column Tumor Targeting
by Siran Aslan, Mohammad Walid Al-Smadi, István Kozma and Árpad Viola
Cancers 2023, 15(24), 5744; https://doi.org/10.3390/cancers15245744 - 7 Dec 2023
Viewed by 1125
Abstract
Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important [...] Read more.
Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of cone beam computed tomography with magnetic resonance imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated the procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed for precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT–MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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10 pages, 4352 KiB  
Article
Correlation Analysis on Anatomical Variants of Accessory Foramina in the Sphenoid Bone for Oncological Surgery
by Andrea Palamenghi, Michaela Cellina, Maurizio Cè, Annalisa Cappella, Chiarella Sforza and Daniele Gibelli
Cancers 2023, 15(22), 5341; https://doi.org/10.3390/cancers15225341 - 9 Nov 2023
Viewed by 829
Abstract
The sphenoid bone presents several anatomical variations, including accessory foramina, such as the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal, which may be involved in tumor invasion or surgery of surrounding structures. Therefore, clinicians and surgeons have [...] Read more.
The sphenoid bone presents several anatomical variations, including accessory foramina, such as the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal, which may be involved in tumor invasion or surgery of surrounding structures. Therefore, clinicians and surgeons have to consider these variants when planning surgical interventions of the cranial base. The prevalence of each variant is reported in the published literature, but very little information is available on the possible correlation among different variants. Here, 300 CT scans of patients (equally divided among males and females) were retrospectively assessed to investigate the presence of the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal. Possible differences in the prevalence of each accessory foramen according to sex were assessed, as well as possible correlations among different variants through the Chi-square test (p < 0.01). Overall, the prevalence of the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal was 30.7%, 67.7%, 14.0% and 35.3%, respectively, without any difference according to sex (p > 0.01). A significant positive correlation was found between the foramen of Vesalius and canaliculus innominatus, both in males and in females (p < 0.01). In detail, subjects with canaliculus innominatus in 85.7–100.0% of cases also showed the foramen of Vesalius, independently from sex and side. The present study provided novel data about the prevalence of four accessory foramina of the sphenoid bone in an Italian population, and a correlation between the foramen of Vesalius and the canaliculus innominatus was found for the first time. As these accessory foramina host neurovascular structures, the results of this study are thus useful for appropriate planning surgical procedures that are tailored to the anatomical configuration of the patient and for improving techniques to avoid accidental injuries in cranial base surgery. Knowledge of the topography, frequencies and the presence/absence of these additional foramina are pivotal for a successful procedure. Clinicians and surgeons may benefit from these novel data for appropriate recognition of the variants, decision-making, pre-operative and treatment planning, improvement of the procedures, screening of patients and prevention of misdiagnosis. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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11 pages, 1302 KiB  
Article
Single-Probe Percutaneous Cryoablation with Liquid Nitrogen for the Treatment of T1a Renal Tumors
by Benjamin Moulin, Tarek Kammoun, Regis Audoual, Stéphane Droupy, Vincent Servois, Paul Meria, Jean paul Beregi and Julien Frandon
Cancers 2023, 15(21), 5192; https://doi.org/10.3390/cancers15215192 - 28 Oct 2023
Cited by 1 | Viewed by 967
Abstract
Kidney cancer accounts for 3% of adult malignancies and is increasingly detected through advanced imaging techniques, highlighting the need for effective treatment strategies. This retrospective study assessed the safety and efficacy of a new single-probe percutaneous cryoablation system using liquid nitrogen for treating [...] Read more.
Kidney cancer accounts for 3% of adult malignancies and is increasingly detected through advanced imaging techniques, highlighting the need for effective treatment strategies. This retrospective study assessed the safety and efficacy of a new single-probe percutaneous cryoablation system using liquid nitrogen for treating T1a renal cancers. From May 2019 to May 2022, 25 consecutive patients from two academic hospitals, with a median age of 64.8 years [IQR 59; 75.5], underwent cryoablation for 26 T1a renal tumors. These tumors had a median size of 25.3 mm [20; 30.7] and a median RENAL nephrometry score, indicating tumor complexity, of 7 [5; 9]. No major complications arose, but three non-clinically relevant perirenal hematomas were detected on post-procedure CT scans. With a median follow-up of 795 days [573; 1020], the primary local control rate at one month stood was 80.8% (21 out of 26). The five recurrent lesions, which exhibited a higher renal score (p = 0.016), were treated again using cryoablation, achieving a secondary local control rate of 100%. No patient died, and the disease-free survival rate was 92% (23 out of 25). In conclusion, single-probe percutaneous cryoablation emerges as a promising modality for managing small renal masses. Notably, recurrence rates appear influenced by RENAL nephrometry scores, suggesting a need for further research to refine the technique. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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14 pages, 10047 KiB  
Article
Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis
by Songhui Diao, Pingjun Chen, Eman Showkatian, Rukhmini Bandyopadhyay, Frank R. Rojas, Bo Zhu, Lingzhi Hong, Muhammad Aminu, Maliazurina B. Saad, Morteza Salehjahromi, Amgad Muneer, Sheeba J. Sujit, Carmen Behrens, Don L. Gibbons, John V. Heymach, Neda Kalhor, Ignacio I. Wistuba, Luisa M. Solis Soto, Jianjun Zhang, Wenjian Qin and Jia Wuadd Show full author list remove Hide full author list
Cancers 2023, 15(19), 4824; https://doi.org/10.3390/cancers15194824 - 1 Oct 2023
Viewed by 1655
Abstract
Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting [...] Read more.
Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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12 pages, 4836 KiB  
Article
Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer
by Chia-Chi Liu, Ya-Fu Cheng, Pei-Cing Ke, Yi-Ling Chen, Ching-Min Lin and Bing-Yen Wang
Cancers 2023, 15(15), 3952; https://doi.org/10.3390/cancers15153952 - 3 Aug 2023
Viewed by 1055
Abstract
Background: Volume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes [...] Read more.
Background: Volume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes according to VDT. Methods: We retrospectively studied 96 cases post sublobar resection from 2012 to 2018, collecting two chest CT scans preoperatively of each case and calculating the VDT. The receiver operating characteristic curve was constructed to identify the optimal cut-off point of VDTs as 133 days. We divided patients into two groups: VDT < 133 days and VDT ≥ 133 days. Univariable and multivariable analyses were performed for comparative purposes. Results: Univariable and multivariable analyses revealed that the consolidation and tumor diameter ratio was the factor of overall survival (OS), and VDT was the only factor of disease-free survival (DFS). The five year OS rates of patients with VDTs ≥ 133 days and VDTs < 133 days, respectively, were 89.9% and 71.9%, and the five year DFS rates were 95.9% and 61.5%. Conclusion: As VDT serves as a powerful prognostic predictor and provides an essential role in planning surgical procedures, the evaluation of VDT preoperatively is highly suggested. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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13 pages, 5381 KiB  
Article
Optimization of Indocyanine Green for Intraoperative Fluorescent Image-Guided Localization of Lung Cancer; Analysis Based on Solid Component of Lung Nodule
by Ok Hwa Jeon, Byeong Hyeon Choi, Jiyun Rho, Kyungsu Kim, Jun Hee Lee, Jinhwan Lee, Beop-Min Kim and Hyun Koo Kim
Cancers 2023, 15(14), 3643; https://doi.org/10.3390/cancers15143643 - 16 Jul 2023
Cited by 1 | Viewed by 1328
Abstract
ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer [...] Read more.
ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer detection using animal models and to evaluate the feasibility of ICG fluorescence in lung cancer patients. In a preclinical study, twenty C57BL/6 mice with footpad cancer and thirty-three rabbits with VX2 lung cancer were used. These animals received an intravenous injection of ICG at 0.5, 1, 2, or 5 mg/kg, and the cancers were detected using a fluorescent imaging system after 3, 6, 12, and 24 h. In a clinical study, fifty-one patients diagnosed with lung cancer and scheduled to undergo surgery were included. Fluorescent images of lung cancer were obtained, and the fluorescent signal was quantified. Based on a preclinical study, the optimal injection method for lung cancer detection was 2 mg/kg ICG 12 h before surgery. Among the 51 patients, ICG successfully detected 37 of 39 cases with a consolidation-to-tumor (C/T) ratio of >50% (TNR: 3.3 ± 1.2), while it failed in 12 cases with a C/T ratio ≤ 50% and 2 cases with anthracosis. ICG injection at 2 mg/kg, 12 h before surgery was optimal for lung cancer detection. Lung cancers with the C/T ratio > 50% were successfully detected using ICG with a detection rate of 95%, but not with the C/T ratio ≤ 50%. Therefore, further research is needed to develop fluorescent agents targeting lung cancer. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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Review

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14 pages, 2546 KiB  
Review
A Perspective Review: Analyzing Collagen Alterations in Ovarian Cancer by High-Resolution Optical Microscopy
by Kristal L. Gant, Manish S. Patankar and Paul J. Campagnola
Cancers 2024, 16(8), 1560; https://doi.org/10.3390/cancers16081560 - 19 Apr 2024
Viewed by 804
Abstract
High-grade serous ovarian cancer (HGSOC) is the predominant subtype of ovarian cancer (OC), occurring in more than 80% of patients diagnosed with this malignancy. Histological and genetic analysis have confirmed the secretory epithelial of the fallopian tube (FT) as a major site of [...] Read more.
High-grade serous ovarian cancer (HGSOC) is the predominant subtype of ovarian cancer (OC), occurring in more than 80% of patients diagnosed with this malignancy. Histological and genetic analysis have confirmed the secretory epithelial of the fallopian tube (FT) as a major site of origin of HGSOC. Although there have been significant strides in our understanding of this disease, early stage detection and diagnosis are still rare. Current clinical imaging modalities lack the ability to detect early stage pathogenesis in the fallopian tubes and the ovaries. However, there are several microscopic imaging techniques used to analyze the structural modifications in the extracellular matrix (ECM) protein collagen in ex vivo FT and ovarian tissues that potentially can be modified to fit the clinical setting. In this perspective, we evaluate and compare the myriad of optical tools available to visualize these alterations and the invaluable insights these data provide on HGSOC initiation. We also discuss the clinical implications of these findings and how these data may help novel tools for early diagnosis of HGSOC. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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14 pages, 594 KiB  
Review
Disparities in Breast Cancer Diagnostics: How Radiologists Can Level the Inequalities
by Filippo Pesapane, Priyan Tantrige, Anna Rotili, Luca Nicosia, Silvia Penco, Anna Carla Bozzini, Sara Raimondi, Giovanni Corso, Roberto Grasso, Gabriella Pravettoni, Sara Gandini and Enrico Cassano
Cancers 2024, 16(1), 130; https://doi.org/10.3390/cancers16010130 - 27 Dec 2023
Cited by 1 | Viewed by 1228
Abstract
Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper [...] Read more.
Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper critically assesses methods to mitigate these disparities, with a focus on breast cancer screening. We underscore scientific mobility as a vital tool for radiologists to advocate for healthcare policy changes: it not only enhances diversity and cultural competence within the radiology community but also fosters international cooperation and knowledge exchange among healthcare institutions. Efforts to ensure cultural competency among radiologists are discussed, including ongoing cultural education, sensitivity training, and workforce diversification. These initiatives are key to improving patient communication and reducing healthcare disparities. This paper also highlights the crucial role of policy changes and legislation in promoting equal access to essential screening services like mammography. We explore the challenges and potential of teleradiology in improving access to medical imaging in remote and underserved areas. In the era of artificial intelligence, this paper emphasizes the necessity of validating its models across a spectrum of populations to prevent bias and achieve equitable healthcare outcomes. Finally, the importance of international collaboration is illustrated, showcasing its role in sharing insights and strategies to overcome global access barriers in medical imaging. Overall, this paper offers a comprehensive overview of the challenges related to disparities in medical imaging access and proposes actionable strategies to address these challenges, aiming for equitable healthcare delivery. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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25 pages, 3352 KiB  
Review
Artificial Intelligence in Lung Cancer Screening: The Future Is Now
by Michaela Cellina, Laura Maria Cacioppa, Maurizio Cè, Vittoria Chiarpenello, Marco Costa, Zakaria Vincenzo, Daniele Pais, Maria Vittoria Bausano, Nicolò Rossini, Alessandra Bruno and Chiara Floridi
Cancers 2023, 15(17), 4344; https://doi.org/10.3390/cancers15174344 - 30 Aug 2023
Cited by 10 | Viewed by 5869
Abstract
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of [...] Read more.
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening. Full article
(This article belongs to the Special Issue Advances in Oncological Imaging)
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