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Tareco Bucho TM, Petrychenko L, Abdelatty MA, Bogveradze N, Bodalal Z, Beets-Tan RG, Trebeschi S. Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation. Eur J Radiol Open 2024; 12:100562. [PMID: 38660370 PMCID: PMC11039940 DOI: 10.1016/j.ejro.2024.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
Background The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.
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Affiliation(s)
- Teresa M. Tareco Bucho
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Liliana Petrychenko
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Mohamed A. Abdelatty
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Radiology, Kasr Al Ainy Hospital, Cairo University, Cairo, Egypt
| | - Nino Bogveradze
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Zuhir Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Regina G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Denmark
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
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2
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Ahmadian M, Bodalal Z, van der Hulst HJ, Vens C, Karssemakers LHE, Bogveradze N, Castagnoli F, Landolfi F, Hong EK, Gennaro N, Pizzi AD, Beets-Tan RGH, van den Brekel MWM, Castelijns JA. Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features. Comput Biol Med 2024; 174:108389. [PMID: 38593640 DOI: 10.1016/j.compbiomed.2024.108389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
PURPOSE To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. METHODS This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance. RESULTS A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. CONCLUSION Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.
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Affiliation(s)
- Milad Ahmadian
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Amsterdam Center for Language and Communication, University of Amsterdam, Amsterdam, the Netherlands.
| | - Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Hedda J van der Hulst
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Conchita Vens
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; School of Cancer Science, University of Glasgow, Glasgow, Scotland, UK
| | - Luc H E Karssemakers
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Department of Radiology, Royal Marsden Hospital, London, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
| | - Federica Landolfi
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Seoul National University Hospital, Seoul, South Korea
| | - Nicolo Gennaro
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Department of Radiology, Northwestern University, Chicago, USA
| | - Andrea Delli Pizzi
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; ITAB - Institute for Advanced Biomedical Technologies, G. d'Annunzio University, Chieti, Italy; Department of Innovative Technologies in Medicine and Dentistry, G. D'Annunzio University, Chieti, Italy
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Michiel W M van den Brekel
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Amsterdam Center for Language and Communication, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jonas A Castelijns
- Department of Radiology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
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3
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Groot Lipman KB, Boellaard TN, de Gooijer CJ, Bogveradze N, Hong EK, Landolfi F, Castagnoli F, Vakhidova N, Smesseim I, van der Heijden F, Beets-Tan RG, Wittenberg R, Bodalal Z, Burgers JA, Trebeschi S. Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients. J Thorac Imaging 2024; 39:165-172. [PMID: 37905941 PMCID: PMC11027965 DOI: 10.1097/rti.0000000000000759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
PURPOSE Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. MATERIALS AND METHODS Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). RESULTS We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19). CONCLUSION We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.
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Affiliation(s)
- Kevin B.W. Groot Lipman
- Department of Radiology
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam
- Technical Medicine, University of Twente, Enschede
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht
| | | | | | - Nino Bogveradze
- Department of Radiology
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht
- Academic Pridon Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, GA
| | - Eun Kyoung Hong
- Department of Radiology
- Seoul National University Hospital, Seoul, South Korea
| | - Federica Landolfi
- Department of Radiology
- Radiology Unit, Sant’Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Francesca Castagnoli
- Department of Radiology
- Department of Radiology, University of Brescia, Brescia, IT
- Department of Radiology, Royal Marsden Hospital, London, UK
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
| | | | - Illaa Smesseim
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam
| | - Ferdi van der Heijden
- Department of Robotics and Mechatronics, University of Twente, Enschede, The Netherlands
| | - Regina G.H. Beets-Tan
- Department of Radiology
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht
- Faculty of Health Sciences, University of Southern Denmark, Odense, DK
| | | | - Zuhir Bodalal
- Department of Radiology
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht
| | - Jacobus A. Burgers
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam
| | - Stefano Trebeschi
- Department of Radiology
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht
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4
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Schurink NW, van Kranen SR, van Griethuysen JJM, Roberti S, Snaebjornsson P, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Peters FP, Bogveradze N, El Khababi N, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer. Eur Radiol 2023; 33:8889-8898. [PMID: 37452176 PMCID: PMC10667134 DOI: 10.1007/s00330-023-09920-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. METHODS Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). RESULTS After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. CONCLUSIONS Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). CLINICAL RELEVANCE STATEMENT Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. KEY POINTS This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.
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Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petur Snaebjornsson
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Schalkhaar, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Femke P Peters
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Vejle, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
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5
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Bodalal Z, Bogveradze N, Ter Beek LC, van den Berg JG, Sanders J, Hofland I, Trebeschi S, Groot Lipman KBW, Storck K, Hong EK, Lebedyeva N, Maas M, Beets-Tan RGH, Gomez FM, Kurilova I. Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases. Insights Imaging 2023; 14:133. [PMID: 37477715 PMCID: PMC10361926 DOI: 10.1186/s13244-023-01474-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 06/27/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM). METHODS A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia. RESULTS Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia. CONCLUSIONS T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms. CRITICAL RELEVANCE STATEMENT Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).
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Affiliation(s)
- Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Leon C Ter Beek
- Department of Medical Physics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jose G van den Berg
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joyce Sanders
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Ingrid Hofland
- Core Facility Molecular Pathology & Biobank, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Kevin B W Groot Lipman
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Koen Storck
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Natalya Lebedyeva
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Fernando M Gomez
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Hospital Clinic-Hospital Sant Joan de Deu, Barcelona, Spain.
| | - Ieva Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
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6
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Bogveradze N, Maas M, El Khababi N, Schurink NW, Lahaye MJ, Bakers FC, Tanis PJ, Kusters M, Beets GL, Beets-Tan RG, Lambregts DM. Pelvic CT in addition to MRI to differentiate between rectal and sigmoid cancer on imaging using the sigmoid take-off as a landmark. Acta Radiol 2023; 64:467-472. [PMID: 35404168 DOI: 10.1177/02841851221091209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The sigmoid take-off (STO) is a recently established landmark to discern rectal from sigmoid cancer on imaging. STO-assessment can be challenging on magnetic resonance imaging (MRI) due to varying axial planes. PURPOSE To establish the benefit of using computed tomography (CT; with consistent axial planes), in addition to MRI, to anatomically classify rectal versus sigmoid cancer using the STO. MATERIAL AND METHODS A senior and junior radiologist retrospectively classified 40 patients with rectal/rectosigmoid cancers using the STO, first on MRI-only (sagittal and oblique-axial views) and then using a combination of MRI and axial CT. Tumors were classified as rectal/rectosigmoid/sigmoid (according to published STO definitions) and then dichotomized into rectal versus sigmoid. Diagnostic confidence was documented using a 5-point scale. RESULTS Adding CT resulted in a change in anatomical tumor classification in 4/40 cases (10%) for the junior reader and in 6/40 cases (15%) for the senior reader. Diagnostic confidence increased significantly after adding CT for the junior reader (mean score 3.85 vs. 4.27; P < 0.001); confidence of the senior reader was not affected (4.28 vs. 4.25; P = 0.80). Inter-observer agreement was similarly good for MRI only (κ=0.77) and MRI + CT (κ=0.76). Readers reached consensus on the classification of rectal versus sigmoid cancer in 78%-85% of cases. CONCLUSION Availability of a consistent axial imaging plane - in the case of this study provided by CT - in addition to a standard MRI protocol with sagittal and oblique-axial imaging views can be helpful to more confidently localize tumors using the STO as a landmark, especially for more junior readers.
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Affiliation(s)
- Nino Bogveradze
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology & Developmental Biology, 5211University of Maastricht, Maastricht, The Netherlands.,Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Monique Maas
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Najim El Khababi
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology & Developmental Biology, 5211University of Maastricht, Maastricht, The Netherlands
| | - Niels W Schurink
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology & Developmental Biology, 5211University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frans Ch Bakers
- Department of Radiology, 199236Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Pieter J Tanis
- Department of Surgery, Amsterdam UMC, Cancer Centre Amsterdam, University of Amsterdam, Amsterdam, The Netherlands.,Department of Surgical Oncology and Gastrointestinal Surgery, 6993Erasmus MC, Rotterdam, The Netherlands
| | - Miranda Kusters
- Department of Surgery, Amsterdam University Medical Centres, Cancer Centre Amsterdam, 1209University of Amsterdam and VU University, Amsterdam, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, 5211University of Maastricht, Maastricht, The Netherlands.,Department of Surgery, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Gh Beets-Tan
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology & Developmental Biology, 5211University of Maastricht, Maastricht, The Netherlands.,Institute of Regional Health Research, University of Southern Denmark, Denmark
| | - Doenja Mj Lambregts
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Bogveradze N, Snaebjornsson P, Grotenhuis BA, van Triest B, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. MRI anatomy of the rectum: key concepts important for rectal cancer staging and treatment planning. Insights Imaging 2023; 14:13. [PMID: 36652149 PMCID: PMC9849549 DOI: 10.1186/s13244-022-01348-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/04/2022] [Indexed: 01/19/2023] Open
Abstract
A good understanding of the MRI anatomy of the rectum and its surroundings is pivotal to ensure high-quality diagnostic evaluation and reporting of rectal cancer. With this pictorial review, we aim to provide an image-based overview of key anatomical concepts essential for treatment planning, response evaluation and post-operative assessment. These concepts include the cross-sectional anatomy of the rectal wall in relation to T-staging; differences in staging and treatment between anal and rectal cancer; landmarks used to define the upper and lower boundaries of the rectum; the anatomy of the pelvic floor and anal canal, the mesorectal fascia, peritoneum and peritoneal reflection; and guides to help discern different pelvic lymph node stations on MRI to properly stage regional and non-regional rectal lymph node metastases. Finally, this review will highlight key aspects of post-treatment anatomy, including the assessment of radiation-induced changes and the evaluation of the post-operative pelvis after different surgical resection and reconstruction techniques.
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Affiliation(s)
- Nino Bogveradze
- grid.430814.a0000 0001 0674 1393Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands ,Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Petur Snaebjornsson
- grid.430814.a0000 0001 0674 1393Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Brechtje A. Grotenhuis
- grid.430814.a0000 0001 0674 1393Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Baukelien van Triest
- grid.430814.a0000 0001 0674 1393Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Max J. Lahaye
- grid.430814.a0000 0001 0674 1393Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Monique Maas
- grid.430814.a0000 0001 0674 1393Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Geerard L. Beets
- grid.5012.60000 0001 0481 6099GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands ,grid.430814.a0000 0001 0674 1393Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G. H. Beets-Tan
- grid.430814.a0000 0001 0674 1393Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands ,grid.10825.3e0000 0001 0728 0170Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Doenja M. J. Lambregts
- grid.430814.a0000 0001 0674 1393Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
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Hong EK, Bodalal Z, Landolfi F, Bogveradze N, Bos P, Park SJ, Lee JM, Beets-Tan R. Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist's performance for T staging? Abdom Radiol (NY) 2022; 47:2739-2746. [PMID: 35661244 DOI: 10.1007/s00261-022-03534-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. METHODS We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. RESULTS The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720-0.839 for combined model and AUC = 0.697, 95% CI = 0.538-0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621-0.777 for combined model and AUC = 0.628, 95% CI = 0.558-0.689 for CT staging only, Boot CI = 0.099). CONCLUSION CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.
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Affiliation(s)
- Eun Kyoung Hong
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
- Seoul National University Hospital, Seoul, South Korea.
| | - Zuhir Bodalal
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Federica Landolfi
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Nino Bogveradze
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Academic Pridon Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Paula Bos
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sae Jin Park
- Seoul National University Hospital, Seoul, South Korea
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
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Lambregts DMJ, Bogveradze N, Blomqvist LK, Fokas E, Garcia-Aguilar J, Glimelius B, Gollub MJ, Konishi T, Marijnen CAM, Nagtegaal ID, Nilsson PJ, Perez RO, Snaebjornsson P, Taylor SA, Tolan DJM, Valentini V, West NP, Wolthuis A, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH. Current controversies in TNM for the radiological staging of rectal cancer and how to deal with them: results of a global online survey and multidisciplinary expert consensus. Eur Radiol 2022; 32:4991-5003. [PMID: 35254485 PMCID: PMC9213337 DOI: 10.1007/s00330-022-08591-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/22/2021] [Accepted: 01/13/2022] [Indexed: 12/17/2022]
Abstract
Abstract
Objectives
To identify the main problem areas in the applicability of the current TNM staging system (8th ed.) for the radiological staging and reporting of rectal cancer and provide practice recommendations on how to handle them.
Methods
A global case-based online survey was conducted including 41 image-based rectal cancer cases focusing on various items included in the TNM system. Cases reaching < 80% agreement among survey respondents were identified as problem areas and discussed among an international expert panel, including 5 radiologists, 6 colorectal surgeons, 4 radiation oncologists, and 3 pathologists.
Results
Three hundred twenty-one respondents (from 32 countries) completed the survey. Sixteen problem areas were identified, related to cT staging in low-rectal cancers, definitions for cT4b and cM1a disease, definitions for mesorectal fascia (MRF) involvement, evaluation of lymph nodes versus tumor deposits, and staging of lateral lymph nodes. The expert panel recommended strategies on how to handle these, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define MRF involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes.
Conclusions
The recommendations derived from this global survey and expert panel discussion may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting.
Key Points
• Via a case-based online survey (incl. 321 respondents from 32 countries), we identified 16 problem areas related to the applicability of the TNM staging system for the radiological staging and reporting of rectal cancer.
• A multidisciplinary panel of experts recommended strategies on how to handle these problem areas, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define mesorectal fascia involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes.
• These recommendations may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting.
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Affiliation(s)
- Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands.
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Lennart K Blomqvist
- Department of Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden
| | - Emmanouil Fokas
- Department of Radiooncology, University Hospital, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
| | - Julio Garcia-Aguilar
- Department of Surgery, Colorectal Service, Benno C. Schmidt Chair in Surgical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bengt Glimelius
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Marc J Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tsuyoshi Konishi
- Department of Colon and Rectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Corrie A M Marijnen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Iris D Nagtegaal
- Department of Pathology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Per J Nilsson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Division of Coloproctology, Pelvic Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Rodrigo O Perez
- Hospital Alemão Oswaldo Cruz & Hospital Beneficência Portuguesa de São Paulo, São Paulo, Brazil
| | - Petur Snaebjornsson
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stuart A Taylor
- Centre for Medical Imaging, University College London Hospital, London, UK
| | - Damian J M Tolan
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Vincenzo Valentini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica S. Cuore, Rome, Italy
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Albert Wolthuis
- Department of Abdominal Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands.
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.
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Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F, El Khababi N, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Beets-Tan RGH, Lambregts DMJ. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 2022; 32:1506-1516. [PMID: 34655313 PMCID: PMC8831294 DOI: 10.1007/s00330-021-08251-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/23/2021] [Accepted: 08/06/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. METHODS T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. RESULTS Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41). CONCLUSIONS Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. KEY POINTS • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.
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Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Deventer, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle Aan Den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
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Bogveradze N, El Khababi N, Schurink NW, van Griethuysen JJM, de Bie S, Bosma G, Cappendijk VC, Geenen RWF, Neijenhuis P, Peterson G, Veeken CJ, Vliegen RFA, Maas M, Lahaye MJ, Beets GL, Beets-Tan RGH, Lambregts DMJ. Evolutions in rectal cancer MRI staging and risk stratification in The Netherlands. Abdom Radiol (NY) 2022; 47:38-47. [PMID: 34605966 PMCID: PMC8776669 DOI: 10.1007/s00261-021-03281-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 11/25/2022]
Abstract
Purpose To analyze how the MRI reporting of rectal cancer has evolved (following guideline updates) in The Netherlands. Methods Retrospective analysis of 712 patients (2011–2018) from 8 teaching hospitals in The Netherlands with available original radiological staging reports that were re-evaluated by a dedicated MR expert using updated guideline criteria. Original reports were classified as “free-text,” “semi-structured,” or “template” and completeness of reporting was documented. Patients were categorized as low versus high risk, first based on the original reports (high risk = cT3-4, cN+, and/or cMRF+) and then based on the expert re-evaluations (high risk = cT3cd-4, cN+, MRF+, and/or EMVI+). Evolutions over time were studied by splitting the inclusion period in 3 equal time periods. Results A significant increase in template reporting was observed (from 1.6 to 17.6–29.6%; p < 0.001), along with a significant increase in the reporting of cT-substage, number of N+ and extramesorectal nodes, MRF invasion and tumor-MRF distance, EMVI, anal sphincter involvement, and tumor morphology and circumference. Expert re-evaluation changed the risk classification from high to low risk in 18.0% of cases and from low to high risk in 1.7% (total 19.7%). In the majority (17.9%) of these cases, the changed risk classification was likely (at least in part) related to use of updated guideline criteria, which mainly led to a reduction in high-risk cT-stage and nodal downstaging. Conclusion Updated concepts of risk stratification have increasingly been adopted, accompanied by an increase in template reporting and improved completeness of reporting. Use of updated guideline criteria resulted in considerable downstaging (of mainly high-risk cT-stage and nodal stage). Graphic abstract ![]()
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Affiliation(s)
- Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Shira de Bie
- Department of Radiology, Deventer Ziekenhuis, Deventer, The Netherlands
| | - Gerlof Bosma
- Department of Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | - Peter Neijenhuis
- Department of Surgery, Alrijne Hospital, Leiderdorp, The Netherlands
| | - Gerald Peterson
- Department of Radiology, Spaarne Gasthuis, Haarlem, The Netherlands
| | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands.
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Bogveradze N, Lambregts DMJ, El Khababi N, Dresen RC, Maas M, Kusters M, Tanis PJ, Beets-Tan RGH. The sigmoid take-off as a landmark to distinguish rectal from sigmoid tumours on MRI: Reproducibility, pitfalls and potential impact on treatment stratification. Eur J Surg Oncol 2021; 48:237-244. [PMID: 34583878 DOI: 10.1016/j.ejso.2021.09.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE The sigmoid take-off (STO) was recently introduced as a preferred landmark, agreed upon by expert consensus recommendation, to discern rectal from sigmoid cancer on imaging. Aim of this study was to assess the reproducibility of the STO, explore its potential treatment impact and identify its main interpretation pitfalls. METHODS Eleven international radiologists (with varying expertise) retrospectively assessed n = 155 patients with previously clinically staged upper rectal/rectosigmoid tumours and re-classified them using the STO as completely below (rectum), straddling the STO (rectosigmoid) or completely above (sigmoid), after which scores were dichotomized as rectum (below/straddling STO) and sigmoid (above STO), being the clinically most relevant distinction. A random subset of n = 48 was assessed likewise by 6 colorectal surgeons. . RESULTS Interobserver agreement (IOA) for the 3-category score ranged from κ0.19-0.82 (radiologists) and κ0.32-0.72 (surgeons), with highest scores for the most experienced radiologists (κ0.69-0.76). Of the 155 cases, 44 (28%) were re-classified by ≥ 80% of radiologists as sigmoid cancers; 36 of these originally received neoadjuvant treatment which in retrospect might have been omitted if the STO had been applied. Main interpretation pitfalls were related to anatomical variations, borderline cases near the STO and angulation of axial imaging planes. CONCLUSIONS Good agreement was reached for experienced radiologists. Despite considerable variation among less-expert readers, use of the STO could have changed treatment in ±1/4 of patients in our cohort. Identified interpretation pitfalls may serve as a basis for teaching and to further optimize MR protocols.
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Affiliation(s)
- Nino Bogveradze
- Dept. of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology & Developmental Biology - University of Maastricht, Maastricht, the Netherlands; Dept. of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Doenja M J Lambregts
- Dept. of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
| | - Najim El Khababi
- Dept. of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology & Developmental Biology - University of Maastricht, Maastricht, the Netherlands
| | | | - Monique Maas
- Dept. of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Miranda Kusters
- Dept. of Surgery, Amsterdam University Medical Centres, Cancer Centre Amsterdam, the Netherlands
| | - Pieter J Tanis
- Dept. of Surgery, Amsterdam University Medical Centres, Cancer Centre Amsterdam, the Netherlands
| | - Regina G H Beets-Tan
- Dept. of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology & Developmental Biology - University of Maastricht, Maastricht, the Netherlands
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Bogveradze N, Hasse F, Mayer P, Rupp C, Tjaden C, Klauss M, Kauczor HU, Weber TF. Is MRCP necessary to diagnose pancreas divisum? BMC Med Imaging 2019; 19:33. [PMID: 31035952 PMCID: PMC6489286 DOI: 10.1186/s12880-019-0329-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
Abstract
Background The purpose of this study is to compare the performance of three-dimensional magnetic resonance cholangiopancreatography (3D-MRCP) with non-MRCP T2-weighted magnetic resonance imaging (MRI) sequences for diagnosis of pancreas divisum (PD). Methods This is a retrospective study of 342 consecutive patients with abdominal MRI including 3D-MRCP. 3D-MRCP was a coronal respiration-navigated T2-weighted sequence with 1.5 mm slice thickness. Non-MRCP T2-weighted sequences were (1) a coronal inversion recovery sequence (TIRM) with 6 mm slice thickness and (2) a transverse single shot turbo spin echo sequence (HASTE) with 4 mm slice thickness. For 3D-MRCP, TIRM, and HASTE, presence of PD and assessment of evaluability were determined in a randomized manner. A consensus read by two radiologists using 3D-MRCP, non-MRCP T2-weighted sequences, and other available imaging sequences served as reference standard for diagnosis of PD. Statistical analysis included performance analysis of 3D-MRCP, TIRM, and HASTE and testing for noninferiority of non-MRCP T2-weighted sequences compared with 3D-MRCP. Results Thirty-three of 342 patients (9.7%) were diagnosed with PD using the reference standard. Sensitivity/specificity of 3D-MRCP for detecting PD were 81.2%/69.7% (p < 0.001). Sensitivity/specificity of TIRM and HASTE were 92.5%/93.9 and 98.1%/97.0%, respectively (p < 0.001 each). Grouped sensitivity/specificity of non-MRCP T2-weighted sequences were 99.8%/91.0%. Non-MRCP T2-weighted sequences were non-inferior to 3D-MRCP alone for diagnosis of PD. 20.2, 7.3%, and 2.3% of 3D-MRCP, TIRM, and HASTE, respectively, were not evaluable due to motion artifacts or insufficient duct depiction. Conclusions Non-MRCP T2-weighted MRI sequences offer high performance for diagnosis of PD and are noninferior to 3D-MRCP alone. Trial registration Not applicable.
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Affiliation(s)
- Nino Bogveradze
- Department of MRI, Research Institute of Clinical Medicine (Todua Clinic), 13 Tevdore mgvdlis St., 0112, Tbilisi, Georgia
| | - Felix Hasse
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany
| | - Philipp Mayer
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany
| | - Christian Rupp
- Department of Gastroenterology, Infectious Diseases, Intoxication, Heidelberg University Hospital, INF 410, 69120, Heidelberg, Germany
| | - Christin Tjaden
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany
| | - Miriam Klauss
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany
| | - Tim Frederik Weber
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, INF 110, 69120, Heidelberg, Germany.
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