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Topical Collection "Advances in Cancer Imaging"

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A topical collection in Diagnostics (ISSN 2075-4418). This collection belongs to the section "Medical Imaging and Theranostics".

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Editor

Dr. Yukako Yagi
E-Mail Website1 Website2
Collection Editor

Director of Pathology Digital Imaging, Warren Alpert Center for Digital & Computational Pathology Associate Attending, Department of Pathology, Memorial Sloan Kettering Cancer Center Associate Attending, Department of Medical Physics, Memorial Sloan Kettering Cancer Center Associate Prof of Pathology and Lab Medicine, Weill Cornell Medicine 1275 York Ave, New York, NY 10065 USA
Interests: multimodal-3D analysis/AI

Topical Collection Information

Cancer imaging is playing an essential role in research discovery and in the detection and determination of the stage and decisions around treatment and evaluation of treatment in the clinical area.

In the last decade, in addition to traditional cancer imaging modalities such as CT and MRI, new imaging technologies have been and are being developed, and those provide further information for high-quality diagnosis and treatment.

Multimodal imaging and computational power with artificial intelligence (AI) have been showing the additional dimension of information around cancer.

The aim of this Special Issue is to help us to better understand current and future cancer imaging technologies and guide how these technologies can improve research, diagnosis, and treatment in cancer.

Specifically, this Special Issue will focus on the roles of multimodality imaging and AI in cancer imaging.

Original research articles and review articles aiming to shed light on important advances in modalities in cancer imaging, including radiological and pathology imaging, are invited.

Dr. Yukako Yagi
Collection Editor

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 collection 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.

Keywords

Computed tomography (CT) and magnetic resonance imaging (MRI)
Molecular and nuclear imaging (PET and SPECT)
Endoscopy
3D Imaging
Digital Pathology
Spatial transcriptomics
Artificial Intelligenve (AI)
Machine learning
Multimodal imaging

Published Papers (3 papers)

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2022

Open AccessArticle

The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy

and

Diagnostics 2022, 12(5), 1212; https://doi.org/10.3390/diagnostics12051212 - 12 May 2022

Cited by 2 | Viewed by 1601

Abstract

This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27–2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90–33.13, p < 0.001; HR: 0.15, 95% CI: 0.03–0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47–28.22, p = 0.013; HR: 31.19, 95% CI: 8.48–114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43–272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes. Full article

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Open AccessArticle

Pathological Evaluation of Rectal Cancer Specimens Using Micro-Computed Tomography

and

Diagnostics 2022, 12(4), 984; https://doi.org/10.3390/diagnostics12040984 - 14 Apr 2022

Cited by 1 | Viewed by 1614

Abstract

Whole-block imaging (WBI) using micro-computed tomography (micro-CT) allows the nondestructive reconstruction of a three-dimensional view of tissues, implying that WBI may be used for accurate pathological evaluation of patients with rectal cancer. HOWEVER, the clinical impact of this approach is unclear. We aimed to clarify the efficacy of WBI in the whole-mount specimens of locally advanced rectal cancer. A total of 237 whole-mount formalin-fixed paraffin-embedded blocks from 13 patients with rectal cancer who underwent surgical treatment were enrolled and scanned with micro-CT to generate three-dimensional images. WBI was evaluated following the conventional pathological review of the corresponding whole-slide imaging (WSI). WBI identified all tumor sites detected using WSI. Furthermore, WBI revealed one additional tumor site, which was not detected using WSI. Tumor resection margin was significantly closer to the soft-tissue edge when measured using WBI (7.7 mm vs. 6.6 mm, p < 0.01). Seventy-six percent of tumor deposits on WSI were changed according to the evidence of tumor interaction with the surrounding tissues confirmed using WBI. Furthermore, WBI revealed 25 additional lymph nodes, six of which were metastatic. The combination of conventional hematoxylin and eosin-stained imaging and WBI may contribute to an accurate pathological assessment. Full article

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Open AccessReview

Radiomics of Biliary Tumors: A Systematic Review of Current Evidence

Francesco Fiz

Visala S Jayakody Arachchige

Matteo Gionso

Ilaria Pecorella

Apoorva Selvam

Dakota Russell Wheeler

Martina Sollini

and

Luca Viganò

Diagnostics 2022, 12(4), 826; https://doi.org/10.3390/diagnostics12040826 - 28 Mar 2022

Cited by 8 | Viewed by 1796

Abstract

Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729–0.900, accuracy = 0.69–0.83), tumor grading (AUC = 0.680–0.890, accuracy = 0.70–0.82), and survival (C-index = 0.673–0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice. Full article

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Published Papers (3 papers)

2022

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