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15 pages, 1783 KiB

Open AccessArticle

Longitudinal Monitoring of Simulated Interstitial Fluid Pressure for Pancreatic Ductal Adenocarcinoma Patients Treated with Stereotactic Body Radiotherapy

by Ramesh Paudyal, Eve LoCastro, Marsha Reyngold, Richard Kinh Do, Amaresha Shridhar Konar, Jung Hun Oh, Abhay Dave, Kenneth Yu, Karyn A. Goodman and Amita Shukla-Dave

Cancers 2021, 13(17), 4319; https://doi.org/10.3390/cancers13174319 - 26 Aug 2021

Cited by 3 | Viewed by 2164

Abstract

The present study aims to monitor longitudinal changes in simulated tumor interstitial fluid pressure (IFP) and velocity (IFV) values using dynamic contrast-enhanced (DCE)-MRI-based computational fluid modeling (CFM) in pancreatic ductal adenocarcinoma (PDAC) patients. Nine PDAC patients underwent MRI, including DCE-MRI, on a 3-Tesla [...] Read more.

The present study aims to monitor longitudinal changes in simulated tumor interstitial fluid pressure (IFP) and velocity (IFV) values using dynamic contrast-enhanced (DCE)-MRI-based computational fluid modeling (CFM) in pancreatic ductal adenocarcinoma (PDAC) patients. Nine PDAC patients underwent MRI, including DCE-MRI, on a 3-Tesla MRI scanner at pre-treatment (TX (0)), after the first fraction of stereotactic body radiotherapy (SBRT, (D1-TX)), and six weeks post-TX (D2-TX). The partial differential equation of IFP formulated from the continuity equation, incorporating the Starling Principle of fluid exchange, Darcy velocity, and volume transfer constant (K^trans), was solved in COMSOL Multiphysics software to generate IFP and IFV maps. Tumor volume (V_t), K^trans, IFP, and IFV values were compared (Wilcoxon and Spearman) between the time- points. D2-TX K^trans values were significantly different from pre-TX and D1-TX (p < 0.05). The D1-TX and pre-TX mean IFV values exhibited a borderline significant difference (p = 0.08). The IFP values varying <3.0% between the three time-points were not significantly different (p > 0.05). V_t and IFP values were strongly positively correlated at pre-TX (ρ = 0.90, p = 0.005), while IFV exhibited a strong negative correlation at D1-TX (ρ = −0.74, p = 0.045). V_t, K^trans, IFP, and IFV hold promise as imaging biomarkers of early response to therapy in PDAC. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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14 pages, 1187 KiB

Open AccessArticle

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer

by Andreas G. Wibmer, Michael W. Kattan, Francesco Alessandrino, Alexander D. J. Baur, Lars Boesen, Felipe Boschini Franco, David Bonekamp, Riccardo Campa, Hannes Cash, Violeta Catalá, Sebastien Crouzet, Sounil Dinnoo, James Eastham, Fiona M. Fennessy, Kamyar Ghabili, Markus Hohenfellner, Angelique W. Levi, Xinge Ji, Vibeke Løgager, Daniel J. Margolis, Paul C. Moldovan, Valeria Panebianco, Tobias Penzkofer, Philippe Puech, Jan Philipp Radtke, Olivier Rouvière, Heinz-Peter Schlemmer, Preston C. Sprenkle, Clare M. Tempany, Joan C. Vilanova, Jeffrey Weinreb, Hedvig Hricak and Amita Shukla-Dave Show full author list Hide full author list

Cancers 2021, 13(11), 2627; https://doi.org/10.3390/cancers13112627 - 27 May 2021

Cited by 14 | Viewed by 4379

Abstract

Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI [...] Read more.

Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. Results: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). Conclusions: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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14 pages, 1745 KiB

Open AccessArticle

Implications of Standardized Uptake Values of Oral Squamous Cell Carcinoma in PET-CT on Prognosis, Tumor Characteristics and Mitochondrial DNA Heteroplasmy

by Lukas Latzko, Bernd Schöpf, Hansi Weissensteiner, Federica Fazzini, Liane Fendt, Eberhard Steiner, Emanuel Bruckmoser, Georg Schäfer, Roy-Cesar Moncayo, Helmut Klocker and Johannes Laimer

Cancers 2021, 13(9), 2273; https://doi.org/10.3390/cancers13092273 - 10 May 2021

Cited by 3 | Viewed by 2367

Abstract

Under aerobic conditions, some cancers switch to glycolysis to cover their energy requirements. Taking advantage of this process, functional imaging techniques such as PET-CT can be used to detect and assess tumorous tissues. The aim of this study was to investigate standardized uptake [...] Read more.

Under aerobic conditions, some cancers switch to glycolysis to cover their energy requirements. Taking advantage of this process, functional imaging techniques such as PET-CT can be used to detect and assess tumorous tissues. The aim of this study was to investigate standardized uptake values and mitochondrial DNA mutations in oral squamous cell carcinoma. A cohort of 57 patients underwent ¹⁸[F]FDG-PET-CT and standardized uptake values were collected. In 15 patients, data on mitochondrial DNA mutations of the tumor were available. Kaplan–Meier curves were calculated, and correlation analyses as well as univariate Cox proportional hazard models were performed. Using ROC analysis to determine a statistical threshold for SUVmax in PET investigations, a cut-off value was determined at 9.765 MB/mL. Survival analysis for SUVmax in these groups showed a Hazard Ratio of 4 (95% CI 1.7–9) in the high SUVmax group with 5-year survival rates of 23.5% (p = 0.00042). For SUVmax and clinicopathological tumor features, significant correlations were found. A tendency towards higher mtDNA heteroplasmy levels in high SUVmax groups could be observed. We were able to confirm the prognostic value of SUVmax in OSCC, showing higher survival rates at lower SUVmax levels. Correlations between SUVmax and distinct tumor characteristics were highly significant, providing evidence that SUVmax may act as a reliable diagnostic parameter. Correlation analysis of mtDNA mutations suggests an influence on metabolic activity in OSCC. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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13 pages, 3734 KiB

Open AccessArticle

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner

by Reza Reiazi, Colin Arrowsmith, Mattea Welch, Farnoosh Abbas-Aghababazadeh, Christopher Eeles, Tony Tadic, Andrew J. Hope, Scott V. Bratman and Benjamin Haibe-Kains

Cancers 2021, 13(9), 2269; https://doi.org/10.3390/cancers13092269 - 8 May 2021

Cited by 14 | Viewed by 2767

Abstract

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. [...] Read more.

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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22 pages, 5233 KiB

Open AccessArticle

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy

by David A. Hormuth II, Angela M. Jarrett, Tessa Davis and Thomas E. Yankeelov

Cancers 2021, 13(8), 1765; https://doi.org/10.3390/cancers13081765 - 7 Apr 2021

Cited by 13 | Viewed by 2819

Abstract

Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize [...] Read more.

Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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15 pages, 3859 KiB

Open AccessArticle

Prospective Image Quality and Lesion Assessment in the Setting of MR-Guided Radiation Therapy of Prostate Cancer on an MR-Linac at 1.5 T: A Comparison to a Standard 3 T MRI

by Haidara Almansour, Saif Afat, Victor Fritz, Fritz Schick, Marcel Nachbar, Daniela Thorwarth, Daniel Zips, Arndt-Christian Müller, Konstantin Nikolaou, Ahmed E. Othman and Daniel Wegener

Cancers 2021, 13(7), 1533; https://doi.org/10.3390/cancers13071533 - 26 Mar 2021

Cited by 16 | Viewed by 3333

Abstract

The objective of this study is to conduct a qualitative and a quantitative image quality and lesion evaluation in patients undergoing MR-guided radiation therapy (MRgRT) for prostate cancer on a hybrid magnetic resonance imaging and linear accelerator system (MR-Linac or MRL) at 1.5 [...] Read more.

The objective of this study is to conduct a qualitative and a quantitative image quality and lesion evaluation in patients undergoing MR-guided radiation therapy (MRgRT) for prostate cancer on a hybrid magnetic resonance imaging and linear accelerator system (MR-Linac or MRL) at 1.5 Tesla. This prospective study was approved by the institutional review board. A total of 13 consecutive patients with biopsy-confirmed prostate cancer and an indication for MRgRT were included. Prior to radiation therapy, each patient underwent an MR-examination on an MRL and on a standard MRI scanner at 3 Tesla (MRI_3T). Three readers (two radiologists and a radiation oncologist) conducted an independent qualitative and quantitative analysis of T2-weighted (T2w) and diffusion-weighted images (DWI). Qualitative outcome measures were as follows: zonal anatomy, capsule demarcation, resolution, visibility of the seminal vesicles, geometric distortion, artifacts, overall image quality, lesion conspicuity, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. Lesion conspicuity and diagnostic confidence were firstly analyzed only on MRL. Afterwards, these outcome parameters were analyzed in consensus with the MRI_3T. Quantitative outcome measures were as follows: anteroposterior and right left diameter of the prostate, lesion size, PI-RADS score (Prostate Imaging—Reporting and Data System) and apparent diffusion coefficient (ADC) of the lesions. Intergroup comparisons were computed using the Wilcoxon-sign rank test and t tests. A post-hoc regression analysis was computed for lesion evaluation. Finally, inter-/intra-reader agreement was analyzed using the Fleiss kappa and intraclass correlation coefficient. For T2w images, the MRL showed good results across all quality criteria (median 3 and 4). Furthermore, there were no significant differences between MRL and MRI_3T regarding capsule demarcation or geometric distortion. For the DWI, the MRL performed significantly less than MRI_3T across most image quality criteria with a median ranging between 2 and 3. However, there were no significant differences between MRL and MRI_3T regarding geometric distortion. In terms of lesion conspicuity and diagnostic confidence, inter-reader agreement was fair for MRL alone (Kappa = 0.42) and good for MRL in consensus with MRI_3T (Kappa = 0.708). Thus, lesion conspicuity and diagnostic confidence could be significantly improved when reading MRL images in consensus with MRI_3T (Odds ratio: 9- to 11-fold for the T2w images and 5- to 8–fold for the DWI) (p < 0.001). For measures of lesion size, anterior-posterior and right-left prostate diameter, inter-reader and intersequence agreement were excellent (ICC > 0.90) and there were no significant differences between MRL and MRI_3T among all three readers. In terms of Prostate Imaging Reporting and Data System (PIRADS) scoring, no significant differences were observed between MRL and MRI_3T. Finally, there was a significant positive linear relationship between lesion ADC measurements (r = 0.76, p < 0.01) between the ADC values measured on both systems. In conclusion, image quality for T2w was comparable and diagnostic even without administration of spasmolytic- or contrast agents, while DWI images did not reach diagnostic level and need to be optimized for further exploitation in the setting of MRgRT. Diagnostic confidence and lesion conspicuity were significantly improved by reading MRL in consensus with MRI_3T which would be advisable for a safe planning and treatment workflow. Finally, ADC measurements of lesions on both systems were comparable indicating that, lesion ADC as measured on the MRL could be used as a biomarker for evaluation of treatment response, similar to examinations using MRI_3T. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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14 pages, 1492 KiB

Open AccessArticle

Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure

by Ramesh Paudyal, Linda Chen, Jung Hun Oh, Kaveh Zakeri, Vaios Hatzoglou, C. Jillian Tsai, Nancy Lee and Amita Shukla-Dave

Cancers 2021, 13(5), 1128; https://doi.org/10.3390/cancers13051128 - 6 Mar 2021

Cited by 4 | Viewed by 2005

Abstract

The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma [...] Read more.

The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics K^trans, v_e, and τ_i were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean v_e values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10⁻³ (mm²/s), D ≤ 0.74 × 10⁻³ (mm²/s), and f ≤ 0.18 (p < 0.05). τ_i ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG’s modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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13 pages, 1499 KiB

Open AccessArticle

Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment

by Chu Hyun Kim, Hyunjin Park, Ho Yun Lee, Joong Hyun Ahn, Seung Hak Lee, Insuk Sohn, Joon Young Choi and Hong Kwan Kim

Cancers 2020, 12(12), 3564; https://doi.org/10.3390/cancers12123564 - 28 Nov 2020

Viewed by 2433

Abstract

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the [...] Read more.

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the pathologic complete response of LNs showing residual uptake after neoadjuvant concurrent chemoradiotherapy (NeoCCRT) in patients with non-small cell lung cancer (NSCLC). Two hundred and thirty-seven patients who underwent NeoCCRT for stage IIIa NSCLC were included. Two hundred fifty-two CT radiomics features were extracted from LNs showing remaining positive FDG uptake upon restaging PET-CT. A multivariable logistic regression analysis of radiomics features and clinicopathologic characteristics was used to develop a prediction model. Of the 237 patients, 135 patients (185 nodes) met our inclusion criteria. Eighty-seven LNs were proven to be malignant (47.0%, 87/185). Upon multivariable analysis, metastatic LNs were significantly prevalent in females and patients with adenocarcinoma (odds ratio (OR) = 2.02, 95% confidence interval (CI) = 0.88–4.62 and OR = 0.39, 95% CI = 0.19–0.77 each). Metastatic LNs also had a larger maximal 3D diameter and higher cluster tendency (OR = 9.92, 95% CI = 3.15–31.17 and OR = 2.36, 95% CI = 1.22–4.55 each). The predictive model for metastasis showed a discrimination performance with an area under the receiver operating characteristic curve of 0.728 (95% CI = 0.654–0.801, p value < 0.001). The radiomics approach allows for the noninvasive detection of metastases in LNs with residual FDG uptake after the treatment of NSCLC patients. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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Review

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18 pages, 968 KiB

Open AccessReview

Transformational Role of Medical Imaging in (Radiation) Oncology

by Catherine Coolens, Matt N. Gwilliam, Paula Alcaide-Leon, Isabella Maria de Freitas Faria and Fabio Ynoe de Moraes

Cancers 2021, 13(11), 2557; https://doi.org/10.3390/cancers13112557 - 23 May 2021

Cited by 5 | Viewed by 3467

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered [...] Read more.

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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25 pages, 727 KiB

Open AccessReview

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

by Chen-Yi Xie, Chun-Lap Pang, Benjamin Chan, Emily Yuen-Yuen Wong, Qi Dou and Varut Vardhanabhuti

Cancers 2021, 13(10), 2469; https://doi.org/10.3390/cancers13102469 - 19 May 2021

Cited by 21 | Viewed by 5451

Abstract

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide [...] Read more.

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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17 pages, 1670 KiB

Open AccessReview

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

by Andrew Hope, Maikel Verduin, Thomas J Dilling, Ananya Choudhury, Rianne Fijten, Leonard Wee, Hugo JWL Aerts, Issam El Naqa, Ross Mitchell, Marc Vooijs, Andre Dekker, Dirk de Ruysscher and Alberto Traverso

Cancers 2021, 13(10), 2382; https://doi.org/10.3390/cancers13102382 - 14 May 2021

Cited by 5 | Viewed by 4127

Abstract

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability [...] Read more.

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients’ data (imaging, electronic health records, patients’ reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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27 pages, 3261 KiB

Open AccessReview

MR Image Changes of Normal-Appearing Brain Tissue after Radiotherapy

by Katharina Witzmann, Felix Raschke and Esther G. C. Troost

Cancers 2021, 13(7), 1573; https://doi.org/10.3390/cancers13071573 - 29 Mar 2021

Cited by 20 | Viewed by 4280

Abstract

Radiotherapy is part of the standard treatment of most primary brain tumors. Large clinical target volumes and physical characteristics of photon beams inevitably lead to irradiation of surrounding normal brain tissue. This can cause radiation-induced brain injury. In particular, late brain injury, such [...] Read more.

Radiotherapy is part of the standard treatment of most primary brain tumors. Large clinical target volumes and physical characteristics of photon beams inevitably lead to irradiation of surrounding normal brain tissue. This can cause radiation-induced brain injury. In particular, late brain injury, such as cognitive dysfunction, is often irreversible and progressive over time, resulting in a significant reduction in quality of life. Since 50% of patients have survival times greater than six months, radiation-induced side effects become more relevant and need to be balanced against radiation treatment given with curative intent. To develop adequate treatment and prevention strategies, the underlying cause of radiation-induced side-effects needs to be understood. This paper provides an overview of radiation-induced changes observed in normal-appearing brains measured with conventional and advanced MRI techniques and summarizes the current findings and conclusions. Brain atrophy was observed with anatomical MRI. Changes in tissue microstructure were seen on diffusion imaging. Vascular changes were examined with perfusion-weighted imaging and susceptibility-weighted imaging. MR spectroscopy revealed decreasing N-acetyl aspartate, indicating decreased neuronal health or neuronal loss. Based on these findings, multicenter prospective studies incorporating advanced MR techniques as well as neurocognitive function tests should be designed in order to gain more evidence on radiation-induced sequelae. Full article

(This article belongs to the Special Issue Transformational Role of Medical Imaging in Oncology)

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