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A special issue of Journal of Personalized Medicine (ISSN 2075-4426).

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 44489

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Prof. Dr. Norman R. Williams

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Surgical & Interventional Trials Unit (SITU), Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House,43-45 Foley St, London W1W 7JN, UK
Interests: biomedical imaging; cancer; data science; global health; human wellbeing; personalised medicine

Dr. Anna Barnes

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Guest Editor

Institute of Nuclear Medicine, University College London Hospitals NHS-Foundation Trust, 235 Euston Rd, London NW1 2BU, UK
Interests: imaging; PET; MRI; biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

The discipline of radiology has been benefiting patients for over 100 years. A myriad of techniques are now available to image the human body both in terms of structure and function. The use of imaging in oncology has radically changed the patient treatment pathway, with the advent of radiomics and an increase in computing power; the use of artificial intelligence (AI) in biomedical imaging and cancers is now a real possibility. This Special Issue will encapsulate the uses of the latest imaging technologies for the purposes of diagnosis, staging, and treatment response in cancer. “Biomedical Imaging and Cancers” aims to cover new hybrid technology for acquisition and novel post-processing of imaging data to enhance and highlight biomarkers for detection of cancer, and the prediction and monitoring of responses to treatment. We are soliciting papers focusing on, but not limited to, new acquisition technology in imaging, novel methods of post processing, and machine learning and deep learning applied to imaging in cancer, biomarkers, and clinical trials.

Prof. Norman R. Williams
Dr. Anna Barnes
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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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

cancer
imaging
machine learning
deep learning
statistics
trials
repeatability
reproducibility
radiomics

Published Papers (12 papers)

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Research

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15 pages, 2251 KiB

Open AccessArticle

Whole Body 3.0 T Magnetic Resonance Imaging in Lymphomas: Comparison of Different Sequence Combinations for Staging Hodgkin’s and Diffuse Large B Cell Lymphomas

by Arash Latifoltojar, Mark K. J. Duncan, Maria Klusmann, Harbir Sidhu, Alan Bainbridge, Deena Neriman, Francesco Fraioli, Jonathan Lambert, Kirit M. Ardeshna and Shonit Punwani

J. Pers. Med. 2020, 10(4), 284; https://doi.org/10.3390/jpm10040284 - 16 Dec 2020

Cited by 5 | Viewed by 2413

Abstract

To investigate the diagnostic value of different whole-body magnetic resonance imaging (WB-MRI) protocols for staging Hodgkin and diffuse-large B-cell lymphomas (HL and DLBCL), twenty-two patients (M/F 12/10, median age 32, range 22–87, HL/DLBCL 14/8) underwent baseline WB-MRI and ¹⁸F-2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography (PET) fused with computed tomography (CT) scan ¹⁸F-FDG-PET-CT. The 3.0 T WB-MRI was performed using pre-contrast modified Dixon (mDixon), T2-weighted turbo-spin-echo (TSE), diffusion-weighted-imaging (DWI), dynamic-contrast-enhanced (DCE) liver/spleen, contrast-enhanced (CE) lung MRI and CE whole-body mDixon. WB-MRI scans were divided into: (1) “WB-MRI _DWI+IP”: whole-body DWI + in-phase mDixon (2) “WB-MRI _T2-TSE”: whole-body T2-TSE (3) “WB-MRI _Post-C”: whole-body CE mDixon + DCE liver/spleen and CE lung mDixon (4) “WB-MRI All “: the entire protocol. Two radiologists evaluated WB-MRIs at random, independently and then in consensus. Two nuclear-medicine-physicians reviewed ¹⁸F-FDG PET-CT in consensus. An enhanced-reference-standard (ERS) was derived using all available baseline and follow-up imaging. The sensitivity and specificity of WB-MRI protocols for nodal and extra-nodal staging was derived against the ERS. Agreement between the WB-MRI protocols and the ERS for overall staging was assessed using kappa statistic. For consensus WB-MRI, the sensitivity and specificity for nodal staging were 75%, 98% for WB-MRI _DWI+IP, 76%, 98% for WB-MRI _Post-C, 83%, 99% for WB-MRI _T2-TSE and 87%, 100% for WB-MRI _All. The sensitivity and specificity for extra-nodal staging were 67% 100% for WB-MRI _DWI+IP, 89%, 100% for WB-MRI _Post-C, 89%, 100% for WB-MRI _T2-TSE and 100%, 100% for the WB-MRI _All. The consensus WB-MRI _All read had perfect agreement with the ERS for overall staging [kappa = 1.00 (95% CI: 1.00-1.00)]. The best diagnostic performance is achieved combining all available WB-MRI sequences. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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19 pages, 17508 KiB

Open AccessArticle

Hybrid PET–MRI Imaging in Paediatric and TYA Brain Tumours: Clinical Applications and Challenges

by Ananth Shankar, Jamshed Bomanji and Harpreet Hyare

J. Pers. Med. 2020, 10(4), 218; https://doi.org/10.3390/jpm10040218 - 09 Nov 2020

Cited by 8 | Viewed by 4241

Abstract

(1) Background: Standard magnetic resonance imaging (MRI) remains the gold standard for brain tumour imaging in paediatric and teenage and young adult (TYA) patients. Combining positron emission tomography (PET) with MRI offers an opportunity to improve diagnostic accuracy. (2) Method: Our single-centre experience of ¹⁸F-fluorocholine (FCho) and ¹⁸fluoro-L-phenylalanine (FDOPA) PET–MRI in paediatric/TYA neuro-oncology patients is presented. (3) Results: Hybrid PET–MRI shows promise in the evaluation of gliomas and germ cell tumours in (i) assessing early treatment response and (ii) discriminating tumour from treatment-related changes. (4) Conclusions: Combined PET–MRI shows promise for improved diagnostic and therapeutic assessment in paediatric and TYA brain tumours. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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14 pages, 1775 KiB

Open AccessArticle

Prognostic Value of CT-Attenuation and ¹⁸F-Fluorodeoxyglucose Uptake of Periprostatic Adipose Tissue in Patients with Prostate Cancer

by Jeong Won Lee, Youn Soo Jeon, Ki Hong Kim, Hee Jo Yang, Chang Ho Lee and Sang Mi Lee

J. Pers. Med. 2020, 10(4), 185; https://doi.org/10.3390/jpm10040185 - 22 Oct 2020

Cited by 6 | Viewed by 2251

Abstract

This study aimed to assess the prognostic value of computed tomography (CT)-attenuation and ¹⁸F-fluorodeoxyglucose (FDG) uptake of periprostatic adipose tissue (PPAT) for predicting disease progression-free survival (DPFS) in patients with prostate cancer. Seventy-seven patients with prostate cancer who underwent staging FDG positron emission tomography (PET)/CT were retrospectively reviewed. CT-attenuation (HU) and FDG uptake (SUV) of PPAT were measured from the PET/CT images. The relationships between these PPAT parameters and clinical factors were assessed, and a Cox proportional hazard regression test was performed to evaluate the prognostic significance of PPAT HU and SUV. PPAT HU and SUV showed significant positive correlations with tumor stage and serum prostate-specific antigen level (PSA) (p < 0.05). Patients with high PPAT HU and SUV had significantly worse DPFS than those with low PPAT HU and SUV (p < 0.05). In multivariate analysis, PPAT SUV was a significant predictor of DPFS after adjusting for tumor stage, serum PSA, and tumor SUV (p = 0.003; hazard ratio, 1.50; 95% confidence interval, 1.15–1.96). CT-attenuation and FDG uptake of PPAT showed significant association with disease progression in patients with prostate cancer. These imaging findings may be evidence of the role of PPAT in prostate cancer progression. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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10 pages, 2098 KiB

Open AccessArticle

Characterisation of MRI Indeterminate Breast Lesions Using Dedicated Breast PET and Prone FDG PET-CT in Patients with Breast Cancer—A Proof-of-Concept Study

by Anmol Malhotra, Sophia Tincey, Vishnu Naidu, Carla Papagiorcopulo, Debashis Ghosh, Peng H. Tan, Fred Wickham and Thomas Wagner

J. Pers. Med. 2020, 10(4), 148; https://doi.org/10.3390/jpm10040148 - 25 Sep 2020

Cited by 3 | Viewed by 2256

Abstract

Magnetic resonance imaging (MRI) in patients with breast cancer to assess extent of disease or multifocal disease can demonstrate indeterminate lesions requiring second-look ultrasound and ultrasound or MRI-guided biopsies. Prone positron emission tomography-computed tomography (PET-CT) is a dedicated acquisition performed with a breast-supporting device on a standard PET-CT scanner. The MAMmography with Molecular Imaging (MAMMI, Oncovision, Valencia, Spain) PET system (PET-MAMMI) is a true tomographic ring scanner for the breast. We investigated if PET-MAMMI and prone PET-CT were able to characterise these MRI- indeterminate lesions further. A total of 10 patients with breast cancer and indeterminate lesions on breast MRI were included. Patients underwent prone PET-MAMMI and prone PET-CT after injection of FDG subsequently on the same day. Patients then resumed their normal pathway, with the clinicians blinded to the results of the PET-MAMMI and prone PET-CT. Of the MRI-indeterminate lesions, eight were histopathologically proven to be malignant and two were benign. PET-MAMMI and prone PET-CT only were able to demonstrate increased FDG uptake in 1/8 and 0/8 of the MRI-indeterminate malignant lesions, respectively. Of the MRI-indeterminate benign lesions, both PET-MAMMI and prone PET-CT demonstrated avidity in 1/2 of these lesions. Our findings do not support the use of PET-MAMMI to characterise indeterminate breast MRI lesions requiring a second look ultrasound. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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11 pages, 2062 KiB

Open AccessArticle

Retrospective CT/MRI Texture Analysis of Rapidly Progressive Hepatocellular Carcinoma

by Charissa Kim, Natasha Cigarroa, Venkateswar Surabhi, Balaji Ganeshan and Anil K. Pillai

J. Pers. Med. 2020, 10(3), 136; https://doi.org/10.3390/jpm10030136 - 21 Sep 2020

Cited by 1 | Viewed by 2926

Abstract

Rapidly progressive hepatocellular carcinoma (RPHCC) is a subset of hepatocellular carcinoma that demonstrates accelerated growth, and the radiographic features of RPHCC versus non-RPHCC have not been determined. The purpose of this retrospective study was to use baseline radiologic features and texture analysis for the accurate detection of RPHCC and subsequent improvement of clinical outcomes. We conducted a qualitative visual analysis and texture analysis, which selectively extracted and enhanced imaging features of different sizes and intensity variation including mean gray-level intensity (mean), standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis at each spatial scaling factor (SSF) value of RPHCC and non-RPHCC tumors in a computed tomography (CT) cohort of n = 11 RPHCC and n = 11 non-RPHCC and a magnetic resonance imaging (MRI) cohort of n = 13 RPHCC and n = 10 non-RPHCC. There was a statistically significant difference across visual CT irregular margins p = 0.030 and CT texture features in SSF between RPHCC and non-RPHCC for SSF-6, coarse-texture scale, mean p = 0.023, SD p = 0.053, MPP p = 0.023. A composite score of mean SSF-6 binarized + SD SSF-6 binarized + MPP SSF-6 binarized + irregular margins was significantly different between RPHCC and non-RPHCC (p = 0.001). A composite score ≥3 identified RPHCC with a sensitivity of 81.8% and specificity of 81.8% (AUC = 0.884, p = 0.002). CT coarse-texture-scale features in combination with visually detected irregular margins were able to statistically differentiate between RPHCC and non-RPHCC. By developing an image-based, non-invasive diagnostic criterion, we created a composite score that can identify RPHCC patients at their early stages when they are still eligible for transplantation, improving the clinical course of patient care. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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14 pages, 1655 KiB

Open AccessArticle

Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism

by Chanwoo Kim, Sang-Ah Han, Kyu Yeoun Won, Il Ki Hong and Deog Yoon Kim

J. Pers. Med. 2020, 10(3), 132; https://doi.org/10.3390/jpm10030132 - 17 Sep 2020

Cited by 7 | Viewed by 2497

Abstract

Cancer stem cells (CSCs) contribute to chemoresistance and tumor relapse. By using the distinct metabolic phenotype of CSC, we designed novel PET parameters for CSC metabolism and investigated their clinical values. Patients with breast cancer who underwent ¹⁸F-FDG PET/CT before neoadjuvant chemotherapy (NAC) were retrospectively included. We developed a method to measure CSC metabolism using standardized uptake value histogram data. The predictive value of novel CSC metabolic parameters for pathologic complete response (pCR) was assessed with multivariable logistic regression. The association between the CSC parameter and disease-free survival (DFS) was also determined. We identified 82 patients with HER2-positive/triple-negative subtypes and 38 patients with luminal tumors. After multivariable analysis, only metabolic tumor volume for CSC (MTVcsc) among metabolic parameters remained the independent predictor of pCR (OR, 0.12; p = 0.022). MTVcsc successfully predicted pathologic tumor response to NAC in HER2-positive/triple-negative subtypes (accuracy, 74%) but not in the luminal subtype (accuracy, 29%). MTVcsc was also predictive of DFS, with a 3-year DFS of 90% in the lower MTVcsc group (<1.75 cm³) versus 72% in the higher group (>1.75 cm³). A novel data-driven PET parameter for CSC metabolism provides early prediction of pCR after NAC and DFS in HER2-positive and triple-negative subtypes. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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13 pages, 1270 KiB

Open AccessArticle

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

by Nguyen Quoc Khanh Le, Duyen Thi Do, Fang-Ying Chiu, Edward Kien Yee Yapp, Hui-Yuan Yeh and Cheng-Yu Chen

J. Pers. Med. 2020, 10(3), 128; https://doi.org/10.3390/jpm10030128 - 15 Sep 2020

Cited by 72 | Viewed by 6500

Abstract

Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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14 pages, 1179 KiB

Open AccessArticle

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

by Roxana-Adelina Lupean, Paul-Andrei Ștefan, Diana Sorina Feier, Csaba Csutak, Balaji Ganeshan, Andrei Lebovici, Bianca Petresc and Carmen Mihaela Mihu

J. Pers. Med. 2020, 10(3), 127; https://doi.org/10.3390/jpm10030127 - 14 Sep 2020

Cited by 10 | Viewed by 3034

Abstract

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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13 pages, 1398 KiB

Open AccessCommunication

Stereotactic Radiotherapy for Brain Metastases: Imaging Tools and Dosimetric Predictive Factors for Radionecrosis

by Marco Lupattelli, Emanuele Alì, Gianluca Ingrosso, Simonetta Saldi, Christian Fulcheri, Simona Borghesi, Roberto Tarducci and Cynthia Aristei

J. Pers. Med. 2020, 10(3), 59; https://doi.org/10.3390/jpm10030059 - 04 Jul 2020

Cited by 13 | Viewed by 4889

Abstract

Radionecrosis (RN) is the most important side effect after stereotactic radiotherapy (SRT) for brain metastases, with a reported incidence ranging from 3% to 24%. To date, there are no unanimously accepted criteria for iconographic diagnosis of RN, as well as no definitive dose-constraints correlated with the onset of this late effect. We reviewed the current literature and gave an overview report on imaging options for the diagnosis of RN and on dosimetric parameters correlated with the onset of RN. We performed a PubMed literature search according to the preferred reporting items and meta-analysis (PRISMA) guidelines, and identified articles published within the last ten years, up to 31 December 2019. When analyzing data on diagnostic tools, perfusion magnetic resonance imaging (MRI) seems to be very useful allowing evaluation of the blood flow in the lesion using the relative cerebral blood volume (rCBV) and blood vessel integrity using relative peak weight (rPH). It is necessary to combine morphological with functional imaging in order to match information about lesion morphology, metabolism and blood-flow. Eventually, serial imaging follow-up is needed. Regarding dosimetric parameters, in radiosurgery (SRS) V12 < 8 cm³ and V10 < 10.5 cm³ of normal brain are the most reliable prognostic factors, whereas in hypo-fractionated stereotactic radiotherapy (HSRT) V18 and V21 are considered the main predictive independent risk factors of RN. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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13 pages, 2356 KiB

Open AccessArticle

Equilibrium CT Texture Analysis for the Evaluation of Hepatic Fibrosis: Preliminary Evaluation against Histopathology and Extracellular Volume Fraction

by Jason Yeung, Balaji Ganeshan, Raymond Endozo, Andrew Hall, Simon Wan, Ashley Groves, Stuart A. Taylor and Steve Bandula

J. Pers. Med. 2020, 10(2), 46; https://doi.org/10.3390/jpm10020046 - 29 May 2020

Cited by 5 | Viewed by 3785

Abstract

Background: Evaluate equilibrium contrast-enhanced CT (EQ-CT) texture analysis (EQ-CTTA) against histologically-quantified fibrosis, serum-based enhanced liver fibrosis panel (ELF) and imaging-based extracellular volume fraction (ECV) in chronic hepatitis. Methods: This study was a re-analysis of image data from a previous prospective study. Pre- and equilibrium-phase post-IV contrast CT datasets were collected from patients with chronic hepatitis with contemporaneous liver biopsy and serum ELF measurement between April 2011 and July 2013. Biopsy samples were analysed to derive collagen proportionate area (CPA). EQ-CTTA was performed with a filtration histogram technique using texture analysis software, with texture quantification using statistical and histogram-based metrics (mean, skewness, standard deviation, entropy, etc.). Association between pre-contrast and EQ-CTTA against CPA, ECV and ELF was evaluated using Spearman’s rank correlation coefficient (r_s). Results: Complete datasets collected in 29 patients (16 male; 13 female), mean age (range): 49 (22–66 years). Liver ECV, CPA and ELF had a median (interquartile range) of 0.26 (0.24–0.29); 5.0 (3.0–13.7) and 9.71 (8.39–10.92). Difference in segment VII hepatic CTTA (medium texture scale) between EQ-CT and pre-contrast images was significantly and positively associated with ELF score (mean: r_s = 0.69, p < 0.001; skewness: r_s = 0.57, p = 0.007). Significant negative associations were observed between pre-contrast and EQ-CT whole hepatic CTTA (coarse texture scale) with CPA (pre-contrast, SD: r_s = −0.66, p < 0.001) and ECV (EQ-CT, entropy: r_s = −0.58, p = 0.006). Conclusions: Hepatic EQ-CTTA demonstrates significant association with validated markers of liver fibrosis, suggesting a role in non-invasive quantification of severity in diffuse fibrosis. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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Review

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27 pages, 1130 KiB

Open AccessReview

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

by Amin Zadeh Shirazi, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca and Guillermo A. Gomez

J. Pers. Med. 2020, 10(4), 224; https://doi.org/10.3390/jpm10040224 - 12 Nov 2020

Cited by 28 | Viewed by 5797

Abstract

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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15 pages, 4093 KiB

Open AccessReview

Personalisation of Molecular Radiotherapy through Optimisation of Theragnostics

by LauraMay Davis, April-Louise Smith, Matthew D. Aldridge, Jack Foulkes, Connie Peet, Simon Wan, Jennifer E. Gains, Jamshed B. Bomanji and Mark N. Gaze

J. Pers. Med. 2020, 10(4), 174; https://doi.org/10.3390/jpm10040174 - 16 Oct 2020

Cited by 7 | Viewed by 2942

Abstract

Molecular radiotherapy, or targeted radionuclide therapy, uses systemically administered drugs bearing a suitable radioactive isotope, typically a beta emitter. These are delivered via metabolic or other physiological pathways to cancer cells in greater concentrations than to normal tissues. The absorbed radiation dose in tumour deposits causes chromosomal damage and cell death. A partner radiopharmaceutical, most commonly the same vector labelled with a different radioactive atom, with emissions suitable for gamma camera or positron emission tomography imaging, is used to select patients for treatment and to assess response. The use of these pairs of radio-labelled drugs, one optimised for therapy, the other for diagnostic purposes, is referred to as theragnostics. Theragnostics is increasingly moving away from a fixed number of defined activity administrations, to a much more individualised or personalised approach, with the aim of improving treatment outcomes, and minimising toxicity. There is, however, still significant scope for further progress in that direction. The main tools for personalisation are the following: imaging biomarkers for better patient selection; predictive and post-therapy dosimetry to maximise the radiation dose to the tumour while keeping organs at risk within tolerance limits; imaging for assessment of treatment response; individualised decision making and communication about radiation protection, adjustments for toxicity, inpatient and outpatient care. Full article

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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