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Cancers
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Quantitative Imaging and Digital Pathology in Clinical Cancer Research
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Quantitative Imaging and Digital Pathology in Clinical Cancer Research

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 4397

Special Issue Editors

Prof. Dr. Nandita deSouza

E-Mail Website
Guest Editor
1. Department of Imaging, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
2. Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SW7 3RP, UK
Interests: magnetic resonance imaging; cervix cancer; prostate cancer; quantitative imaging
Prof. Dr. Katja Pinker-Domenig

E-Mail Website
Guest Editor
Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th St., New York, NY 10065, USA
Interests: breast cancer; breast imaging; women's health; PET/MRI; MRI; DWI; hybrid imaging; radiomics; radiogenomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomarkers provide useful prognostic, predictive, and response assessment information and are of vital importance in directing management pathways in patients with cancer. Blood biomarkers are invaluable but do not represent an individual lesion, nor do they capture disease heterogeneity. Increasingly, therefore, biomarkers derived from imaging datasets or extracted from digitised histologic sections are essential in informing specific management decisions. This Special Issue focuses on a range of imaging-derived and digital-pathology-derived biomarkers and their clinical application in providing diagnostic, prognostic, or treatment response information. Original research is sought that addresses either the clinical utility of imaging biomarkers within a clinical research or clinical trial setting and/or the contribution of digital pathology in diagnostic classification and determining the treatment response. We wish to include a range of imaging and digital pathology biomarkers for a variety of clinical applications within this Special Issue. The use of validated techniques and multicentre data to answer specific clinical questions is particularly welcome. Review articles will be commissioned by the editors as appropriate. The aim is to bring the use of validated imaging and digital pathology-derived biomarkers to a wide audience of end-users, so that these biomarkers can be effectively incorporated into clinical trials and ultimately into clinical practice.

Prof. Dr. Nandita deSouza
Prof. Dr. Katja Pinker-Domenig
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

  • quantitative imaging
  • computerised tomography
  • magnetic resonance imaging
  • positron emission tomography
  • ultrasound
  • digital pathology
  • biomarker

Published Papers (4 papers)

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Editorial

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4 pages, 187 KiB  
Editorial
Delivering a Quantitative Imaging Agenda
by Nandita M. deSouza, Aad van der Lugt, Timothy J. Hall, Daniel Sullivan and Gudrun Zahlmann
Cancers 2023, 15(17), 4219; https://doi.org/10.3390/cancers15174219 - 23 Aug 2023
Cited by 1 | Viewed by 716
Abstract
In a digital image, each voxel contains quantitative information dependent on the technique used to generate the image [...] Full article
(This article belongs to the Special Issue Quantitative Imaging and Digital Pathology in Clinical Cancer Research)

Research

Jump to: Editorial

14 pages, 6600 KiB  
Article
Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms
by Giuseppe D’Abbronzo, Antonio D’Antonio, Annarosaria De Chiara, Luigi Panico, Lucianna Sparano, Anna Diluvio, Antonello Sica, Gino Svanera, Renato Franco and Andrea Ronchi
Cancers 2024, 16(9), 1687; https://doi.org/10.3390/cancers16091687 - 26 Apr 2024
Viewed by 360
Abstract
The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the [...] Read more.
The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University “Luigi Vanvitelli” in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists’ evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners. Full article
(This article belongs to the Special Issue Quantitative Imaging and Digital Pathology in Clinical Cancer Research)
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11 pages, 894 KiB  
Article
Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer
by Valeria Romeo, Panagiotis Kapetas, Paola Clauser, Sazan Rasul, Renato Cuocolo, Martina Caruso, Thomas H. Helbich, Pascal A. T. Baltzer and Katja Pinker
Cancers 2023, 15(20), 5088; https://doi.org/10.3390/cancers15205088 - 21 Oct 2023
Cited by 2 | Viewed by 1075
Abstract
In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced [...] Read more.
In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis. Full article
(This article belongs to the Special Issue Quantitative Imaging and Digital Pathology in Clinical Cancer Research)
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15 pages, 874 KiB  
Article
MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)
by Alan Jackson, Ryan Pathak, Nandita M. deSouza, Yan Liu, Bart K. M. Jacobs, Saskia Litiere, Maria Urbanowicz-Nijaki, Catherine Julie, Arturo Chiti, Jens Theysohn, Juan R. Ayuso, Sigrid Stroobants and John C. Waterton
Cancers 2023, 15(14), 3580; https://doi.org/10.3390/cancers15143580 - 12 Jul 2023
Cited by 1 | Viewed by 1554
Abstract
Background: Tumour apparent diffusion coefficient (ADC) from diffusion-weighted magnetic resonance imaging (MRI) is a putative pharmacodynamic/response biomarker but the relationship between drug-induced effects on the ADC and on the underlying pathology has not been adequately defined. Hypothesis: Changes in ADC during early chemotherapy [...] Read more.
Background: Tumour apparent diffusion coefficient (ADC) from diffusion-weighted magnetic resonance imaging (MRI) is a putative pharmacodynamic/response biomarker but the relationship between drug-induced effects on the ADC and on the underlying pathology has not been adequately defined. Hypothesis: Changes in ADC during early chemotherapy reflect underlying histological markers of tumour response as measured by tumour regression grade (TRG). Methods: Twenty-six patients were enrolled in the study. Baseline, 14 days, and pre-surgery MRI were performed per study protocol. Surgical resection was performed in 23 of the enrolled patients; imaging-pathological correlation was obtained from 39 lesions from 21 patients. Results: There was no evidence of correlation between TRG and ADC changes at day 14 (study primary endpoint), and no significant correlation with other ADC metrics. In scans acquired one week prior to surgery, there was no significant correlation between ADC metrics and percentage of viable tumour, percentage necrosis, percentage fibrosis, or Ki67 index. Conclusions: Our hypothesis was not supported by the data. The lack of meaningful correlation between change in ADC and TRG is a robust finding which is not explained by variability or small sample size. Change in ADC is not a proxy for TRG in metastatic colorectal cancer. Full article
(This article belongs to the Special Issue Quantitative Imaging and Digital Pathology in Clinical Cancer Research)
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