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Magbanua MJM, Li W, van ’t Veer LJ. Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes. Cancers (Basel) 2024; 16:1879. [PMID: 38791958 PMCID: PMC11120531 DOI: 10.3390/cancers16101879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.
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Affiliation(s)
- Mark Jesus M. Magbanua
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA 94115, USA;
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94115, USA;
| | - Laura J. van ’t Veer
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA 94115, USA;
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Haag F, Hertel A, Tharmaseelan H, Kuru M, Haselmann V, Brochhausen C, Schönberg SO, Froelich MF. Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment. ROFO-FORTSCHR RONTG 2024; 196:262-272. [PMID: 37944935 DOI: 10.1055/a-2175-4622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology. KEY POINTS:: · Tumor heterogeneity can be described within one lesion (intralesional) or between several lesions (interlesional).. · The heterogeneous biology of tumor cells can lead to a mixed therapeutic response and should be addressed in diagnostics and the therapeutic regime.. · Quantitative image diagnostics can be enhanced using AI, improved histopathological methods, and liquid profiling in the future..
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Affiliation(s)
- Florian Haag
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Alexander Hertel
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Hishan Tharmaseelan
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Mustafa Kuru
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Verena Haselmann
- Institute of Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, University Hospital Mannheim, Germany
| | - Christoph Brochhausen
- Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefan O Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Matthias F Froelich
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
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Tharmaseelan H, Vellala AK, Hertel A, Tollens F, Rotkopf LT, Rink J, Woźnicki P, Ayx I, Bartling S, Nörenberg D, Schoenberg SO, Froelich MF. Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning. Cancer Imaging 2023; 23:95. [PMID: 37798797 PMCID: PMC10557291 DOI: 10.1186/s40644-023-00612-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/17/2023] [Indexed: 10/07/2023] Open
Abstract
OBJECTIVES The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions. METHODS In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis. RESULTS The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83. CONCLUSIONS CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.
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Affiliation(s)
- Hishan Tharmaseelan
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Abhinay K Vellala
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Lukas T Rotkopf
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
- German Cancer Research Center, E010 Radiology, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Johann Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Piotr Woźnicki
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Isabelle Ayx
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Sönke Bartling
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
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Li X, Chen J, Zhang C, Han Z, Zheng X, Cao D. Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma. BMC Pulm Med 2023; 23:339. [PMID: 37697337 PMCID: PMC10494384 DOI: 10.1186/s12890-023-02609-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images. METHODS This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram. RESULTS Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130. CONCLUSION The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.
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Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Jianwei Chen
- Department of Radiology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, 350014, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai200062, China
| | - Zewen Han
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Xiuying Zheng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China.
- Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, Fuzhou, Fujian, 350212, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Shanghai200062, China.
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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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Shi W, Yang Z, Zhu M, Zou C, Li J, Liang Z, Wang M, Yu H, Yang B, Wang Y, Li C, Wang Z, Zhao W, Chen L. Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules. Front Oncol 2022; 12:986579. [PMID: 36176405 PMCID: PMC9513584 DOI: 10.3389/fonc.2022.986579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundImmunotherapy might be a promising auxiliary or alternative systemic treatment for early-stage lung adenocarcinomas manifesting as ground-glass nodules (GGNs). This study intended to investigate the PD-L1 expression in these patients, and to explore the non-invasive prediction model of PD-L1 expression based on radiomics.MethodsWe retrospectively analyzed the PD-L1 expression of patients with postoperative pathological diagnosis of lung adenocarcinomas and with imaging manifestation of GGNs, and divided patients into positive group and negative group according to whether PD-L1 expression ≥1%. Then, CT-based radiomic features were extracted semi-automatically, and feature dimensions were reduced by univariate analysis and LASSO in the randomly selected training cohort (70%). Finally, we used logistic regression algorithm to establish the radiomic models and the clinical-radiomic combined models for PD-L1 expression prediction, and evaluated the prediction efficiency of the models with the receiver operating characteristic (ROC) curves.ResultsA total of 839 “GGN-like lung adenocarcinoma” patients were included, of which 226 (26.9%) showed positive PD-L1 expression. 779 radiomic features were extracted, and 9 of them were found to be highly corelated with PD-L1 expression. The area under the curve (AUC) values of the radiomic models were 0.653 and 0.583 in the training cohort and test cohort respectively. After adding clinically significant and statistically significant clinical features, the efficacy of the combined model was slightly improved, and the AUC values were 0.693 and 0.598 respectively.ConclusionsGGN-like lung adenocarcinoma had a fairly high positive PD-L1 expression rate. Radiomics was a hopeful noninvasive method for predicting PD-L1 expression, with better predictive efficacy in combination with clinical features.
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Affiliation(s)
- Wenjia Shi
- Department of Respiratory and Critical Medicine, Medical School of Chinese People’s Liberation Army, Beijing, China
| | - Zhen Yang
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Minghui Zhu
- Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chenxi Zou
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jie Li
- Department of Pathology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Zhixin Liang
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Miaoyu Wang
- Department of Respiratory and Critical Medicine, Medical School of Chinese People’s Liberation Army, Beijing, China
| | - Hang Yu
- Department of Respiratory and Critical Medicine, Medical School of Chinese People’s Liberation Army, Beijing, China
| | - Bo Yang
- Department of Thoracic Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Yulin Wang
- Department of Radiology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Chunsun Li
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Zirui Wang
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
- *Correspondence: Wei Zhao, ; Liang’an Chen,
| | - Liang’an Chen
- Department of Respiratory and Critical Medicine, Chinese People’s Liberation Army General Hospital, Beijing, China
- *Correspondence: Wei Zhao, ; Liang’an Chen,
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Maniar A, Wei AZ, Dercle L, Bien HH, Fojo T, Bates SE, Schwartz LH. Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA. Semin Oncol 2022; 49:S0093-7754(22)00042-2. [PMID: 35914982 DOI: 10.1053/j.seminoncol.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/06/2022] [Indexed: 11/11/2022]
Abstract
Current radiographic methods of measuring treatment response for patients with nonsmall cell lung cancer have significant limitations. Recently, new modalities using standard of care images or minimally invasive blood-based DNA tests have gained interest as methods of evaluating treatment response. This article highlights three emerging modalities: radiomic analysis, kinetic analysis and serum-based measurement of circulating tumor DNA, with a focus on the clinical evidence supporting these methods. Additionally, we discuss the possibility of combining these modalities to develop a robust biomarker with strong correlation to clinically meaningful outcomes that could impact clinical trial design and patient care. At Last, we focus on how these methods specifically apply to a Veteran population.
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Affiliation(s)
- Ashray Maniar
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY
| | - Alexander Z Wei
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY
| | - Laurent Dercle
- Columbia University Irving Medical Center, Division of Radiology, New York, NY
| | - Harold H Bien
- Northport VA Medical Center, Division of Hematology and Oncology, Northport, NY
| | - Tito Fojo
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY; James J. Peters Bronx VA Medical Center, Division of Hematology and Oncology, Bronx, NY
| | - Susan E Bates
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY; Northport VA Medical Center, Division of Hematology and Oncology, Northport, NY.
| | - Lawrence H Schwartz
- Columbia University Irving Medical Center, Division of Radiology, New York, NY
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Vigil N, Barry M, Amini A, Akhloufi M, Maldague XPV, Ma L, Ren L, Yousefi B. Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging. Cancers (Basel) 2022; 14:cancers14112663. [PMID: 35681643 PMCID: PMC9179519 DOI: 10.3390/cancers14112663] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/01/2023] Open
Abstract
Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.
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Affiliation(s)
- Nicolle Vigil
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA; (N.V.); (M.B.); (L.M.)
| | - Madeline Barry
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA; (N.V.); (M.B.); (L.M.)
| | - Arya Amini
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA;
| | - Moulay Akhloufi
- Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada;
| | - Xavier P. V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada;
| | - Lan Ma
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA; (N.V.); (M.B.); (L.M.)
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA;
| | - Bardia Yousefi
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA; (N.V.); (M.B.); (L.M.)
- Correspondence:
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Lee T, Rawding PA, Bu J, Hyun S, Rou W, Jeon H, Kim S, Lee B, Kubiatowicz LJ, Kim D, Hong S, Eun H. Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC). Cancers (Basel) 2022; 14:2061. [PMID: 35565192 PMCID: PMC9103537 DOI: 10.3390/cancers14092061] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/04/2022] [Accepted: 04/12/2022] [Indexed: 02/08/2023] Open
Abstract
(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients' survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.
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Affiliation(s)
- Taehee Lee
- Department of Biomedical Laboratory Science, Daegu Health College, Daegu 41453, Korea;
- Department of Senior Healthcare, Graduate School, Eulji University, Uijeongbu-si 11759, Korea;
| | - Piper A. Rawding
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
- Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Korea
- Industry-Academia Interactive R&E Center for Bioprocess Innovation, Inha University, Incheon 22212, Korea
| | - Sunghee Hyun
- Department of Senior Healthcare, Graduate School, Eulji University, Uijeongbu-si 11759, Korea;
| | - Woosun Rou
- Department of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, Korea; (W.R.); (H.J.)
| | - Hongjae Jeon
- Department of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, Korea; (W.R.); (H.J.)
| | - Seokhyun Kim
- Department of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Korea; (S.K.); (B.L.)
| | - Byungseok Lee
- Department of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Korea; (S.K.); (B.L.)
| | - Luke J. Kubiatowicz
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
| | - Dawon Kim
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
- Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, Seoul 03722, Korea
| | - Hyuksoo Eun
- Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, Seoul 03722, Korea
- Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Korea
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Froelich MF, Capoluongo E, Kovacs Z, Patton SJ, Lianidou ES, Haselmann V. The value proposition of integrative diagnostics for (early) detection of cancer. On behalf of the EFLM interdisciplinary Task and Finish Group "CNAPS/CTC for early detection of cancer". Clin Chem Lab Med 2022; 60:821-829. [PMID: 35218176 DOI: 10.1515/cclm-2022-0129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/12/2022]
Abstract
Disruptive imaging and laboratory technologies can improve clinical decision processes and outcomes in oncology. However, certain obstacles must be overcome before these technologies can be fully implemented as part of the standard for care. An integrative diagnostic approach represents a unique opportunity to unleash the full diagnostic potential and paves the way towards personalized cancer diagnostics. To meet this demand, an interdisciplinary Task Force of the EFLM was initiated as a consequence of an EFLM/ESR during the CELME 2019 meeting in order to evaluate the clinical value of CNAPS/CTC (circulating nucleic acids in plasma and serum/circulating tumor cells) in early detection of cancer. Here, an overview of current disruptive techniques, their clinical implications and potential value of an integrative diagnostic approach is provided. Furthermore, requirements such as the establishment of diagnostic tumor boards, development of adequate software solutions and a change of mindset towards a new generation of diagnosticians providing actionable health information are presented. This development has the potential to elevate the position and clinical recognition of diagnosticians.
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Affiliation(s)
- Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medicine Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Mannheim, Germany
| | - Ettore Capoluongo
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Naples, Italy
- CEINGE-Biotecnologie Avanzate, Napoli, Italy
| | - Zsolt Kovacs
- Department of Pathology, Clinical County Emergency Hospital, Targu-Mures, Romania
| | | | - Evi S Lianidou
- Department of Chemistry, Analysis of Circulating Tumor Cells Lab, Laboratory of Analytical Chemistry, University of Athens, Athens, Greece
| | - Verena Haselmann
- Medical Faculty Mannheim of the University of Heidelberg, Institute of Clinical Chemistry, University Hospital Mannheim, Mannheim, Germany
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11
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Magnuska ZA, Theek B, Darguzyte M, Palmowski M, Stickeler E, Schulz V, Kießling F. Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification. Cancers (Basel) 2022; 14:277. [PMID: 35053441 PMCID: PMC8773857 DOI: 10.3390/cancers14020277] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 12/30/2021] [Indexed: 02/04/2023] Open
Abstract
Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola-Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola-Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.
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Affiliation(s)
- Zuzanna Anna Magnuska
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Benjamin Theek
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Milita Darguzyte
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Moritz Palmowski
- Radiologie Baden-Baden, Beethovenstraße 2, 76530 Baden-Baden, Germany;
| | - Elmar Stickeler
- Department of Obstetrics and Gynecology, University Clinic Aachen, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany;
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Volkmar Schulz
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
- Physics Institute III B, RWTH Aachen University, 52074 Aachen, Germany
- Hyperion Hybrid Imaging Systems GmbH, 52074 Aachen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
| | - Fabian Kießling
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
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Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography. Cancers (Basel) 2021; 13:cancers13235985. [PMID: 34885094 PMCID: PMC8657389 DOI: 10.3390/cancers13235985] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 12/04/2022] Open
Abstract
Simple Summary Discovery of predictive and prognostic radiomic features in cancer is currently of great interest to the radiologic and oncologic community. Tumor phenotypic and prognostic information can be obtained by extracting features on tumor segmentations, and it is typically imaging analysts, physician trainees, and attending physicians who provide these labeled datasets for analysis. The potential impact of level and type of specialty training on interobserver variability in manual segmentation of NSCLC was examined. Although there was some variability in segmentation between readers, the subsequently extracted radiomic features were overall well correlated. High fidelity radiomic feature extraction relies on accurate feature extraction from imaging that produce robust prognostic and predictive radiomic NSCLC biomarkers. This study concludes that this goal can be obtained using segmenters of different levels of training and clinical experience. Abstract This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen–Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients (p-value < 0.005). This supports that readers’ level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.
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Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas. Cancers (Basel) 2021; 13:cancers13215463. [PMID: 34771625 PMCID: PMC8582405 DOI: 10.3390/cancers13215463] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 01/07/2023] Open
Abstract
The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) versus intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), which is considered a major challenge for pathological diagnosis. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing. A deep neural network was trained to detect and segment the nuclei of lymphocytes. Quantitative nuclear morphometrics were further computed from these predicted contours. A decision-tree-based machine learning algorithm, XGBoost, was then trained to classify PITL cases into two disease subtypes using these nuclear morphometric features. The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In terms of classifying MEITL versus ITCL-NOS, the XGBoost model achieved an area under receiver operating characteristic curve (AUC) of 0.966. Our research demonstrated an accurate, human-interpretable approach to using machine learning algorithms for reducing the high dimensionality of image features and classifying T cell lymphomas that present challenges in morphologic diagnosis. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status.
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Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
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Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- XiangYa School of Medicine, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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