1
|
van der Reijd DJ, Guerendel C, Staal FCR, Busard MP, De Oliveira Taveira M, Klompenhouwer EG, Kuhlmann KFD, Moelker A, Verhoef C, Starmans MPA, Lambregts DMJ, Beets-Tan RGH, Benson S, Maas M. Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation. Eur Radiol 2023:10.1007/s00330-023-10417-5. [PMID: 37987835 DOI: 10.1007/s00330-023-10417-5] [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] [Received: 07/07/2023] [Revised: 07/07/2023] [Accepted: 09/10/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM). MATERIALS AND METHODS Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation. RESULTS The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively. CONCLUSION The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation. CLINICAL RELEVANCE STATEMENT Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts. KEY POINTS • Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.
Collapse
Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Corentin Guerendel
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Femke C R Staal
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Milou P Busard
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Mateus De Oliveira Taveira
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Elisabeth G Klompenhouwer
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Koert F D Kuhlmann
- Department of Surgery, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Adriaan Moelker
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Campusvej 55, DK 5230, Odense M, Denmark
| | - Sean Benson
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| |
Collapse
|
2
|
Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
Collapse
Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, 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 Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| |
Collapse
|
3
|
Staal FCR, Taghavi M, van der Reijd DJ, Gomez FM, Imani F, Klompenhouwer EG, Meek D, Roberti S, de Boer M, Lambregts DMJ, Beets-Tan RGH, Maas M. Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone. Eur J Radiol 2021; 141:109773. [PMID: 34022475 DOI: 10.1016/j.ejrad.2021.109773] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 04/23/2021] [Accepted: 05/10/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess whether CT-based radiomics of the ablation zone (AZ) can predict local tumour progression (LTP) after thermal ablation for colorectal liver metastases (CRLM). MATERIALS AND METHODS Eighty-two patients with 127 CRLM were included. Radiomics features (with different filters) were extracted from the AZ and a 10 mm periablational rim (PAR)on portal-venous-phase CT up to 8 weeks after ablation. Multivariable stepwise Cox regression analyses were used to predict LTP based on clinical and radiomics features. Performance (concordance [c]-statistics) of the different models was compared and performance in an 'independent' dataset was approximated with bootstrapped leave-one-out-cross-validation (LOOCV). RESULTS Thirty-three lesions (26 %) developed LTP. Median follow-up was 21 months (range 6-115). The combined model, a combination of clinical and radiomics features, included chemotherapy (HR 0.50, p = 0.024), cT-stage (HR 10.13, p = 0.016), lesion size (HR 1.11, p = <0.001), AZ_Skewness (HR 1.58, p = 0.016), AZ_Uniformity (HR 0.45, p = 0.002), PAR_Mean (HR 0.52, p = 0.008), PAR_Skewness (HR 1.67, p = 0.019) and PAR_Uniformity (HR 3.35, p < 0.001) as relevant predictors for LTP. The predictive performance of the combined model (after LOOCV) yielded a c-statistic of 0.78 (95 %CI 0.65-0.87), compared to the clinical or radiomics models only (c-statistic 0.74 (95 %CI 0.58-0.84) and 0.65 (95 %CI 0.52-0.83), respectively). CONCLUSION Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. CT-based radiomics of the AZ and PAR may have potential to aid in the prediction of LTP during follow-up in patients with CRLM.
Collapse
Affiliation(s)
- F C R Staal
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands.
| | - M Taghavi
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - D J van der Reijd
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - F M Gomez
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; Department of Radiology, Hospital Clinic de Barcelona, Carrer de Villarroel, 170, 08036 Barcelona, Spain
| | - F Imani
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - E G Klompenhouwer
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - D Meek
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - S Roberti
- Department of Epidemiology and Biostatistics, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - M de Boer
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - D M J Lambregts
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - R G H Beets-Tan
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands; Institute of Regional Health Research, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
| | - M Maas
- Department of Radiology, Antoni van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
| |
Collapse
|
4
|
van Treijen MJC, van der Zee D, Heeres BC, Staal FCR, Vriens MR, Saveur LJ, Verbeek WHM, Korse CM, Maas M, Valk GD, Tesselaar MET. Blood Molecular Genomic Analysis Predicts the Disease Course of Gastroenteropancreatic Neuroendocrine Tumor Patients: A Validation Study of the Predictive Value of the NETest®. Neuroendocrinology 2021; 111:586-598. [PMID: 32492680 DOI: 10.1159/000509091] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/02/2020] [Indexed: 11/19/2022]
Abstract
Reliable prediction of disease status is a major challenge in managing gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The aim of the study was to validate the NETest®, a blood molecular genomic analysis, for predicting the course of disease in individual patients compared to chromogranin A (CgA). NETest® score (normal ≤20%) and CgA level (normal <100 µg/L) were measured in 152 GEP-NETs. The median follow-up was 36 (4-56) months. Progression-free survival was blindly assessed (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1). Optimal cutoffs (area under the receiver operating characteristic curve [AUC]), odds ratios, as well as negative and positive predictive values (NPVs/PPVs) were calculated for predicting stable disease (SD) and progressive disease (PD). Of the 152 GEP-NETs, 86% were NETest®-positive and 52% CgA-positive. -NETest® AUC was 0.78 versus CgA 0.73 (p = ns). The optimal cutoffs for predicting SD/PD were 33% for the NETest® and 140 µg/L for CgA. Multivariate analyses identified NETest® as the strongest predictor for PD (odds ratio: 5.7 [score: 34-79%]; 12.6 [score: ≥80%]) compared to CgA (odds ratio: 3.0), tumor grade (odds ratio: 3.1), or liver metastasis (odds ratio: 7.7). The NETest® NPV for SD was 87% at 12 months. The PPV for PD was 47 and 64% (scores 34-79% and ≥80%, respectively). NETest® metrics were comparable in the watchful waiting, treatment, and no evidence of disease (NED) subgroups. For CgA (>140 ng/mL), NPV and PPV were 83 and 52%. CgA could not predict PD in the watchful waiting or NED subgroups. The NETest® reliably predicted SD and was the strongest predictor of PD. CgA had lower utility. The -NETest® anticipates RECIST-defined disease status up to 1 year before imaging alterations are apparent.
Collapse
Affiliation(s)
- Mark J C van Treijen
- Department of Endocrine Oncology, University Medical Center Utrecht, Utrecht, The Netherlands,
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands,
| | | | - Birthe C Heeres
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Femke C R Staal
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Menno R Vriens
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Endocrine Surgical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lisette J Saveur
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Gastroenterology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Wieke H M Verbeek
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Gastroenterology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Catharina M Korse
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Clinical Chemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Monique Maas
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Gerlof D Valk
- Department of Endocrine Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Margot E T Tesselaar
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, Netherlands Cancer Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| |
Collapse
|
5
|
Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer 2020; 20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [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: 01/02/2020] [Revised: 09/03/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
Collapse
Affiliation(s)
- Femke C R Staal
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Denise J van der Reijd
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Marjaneh Taghavi
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| |
Collapse
|
6
|
Staal FCR, Ponniah AJT, Angullia F, Ruff C, Koudstaal MJ, Dunaway D. Describing Crouzon and Pfeiffer syndrome based on principal component analysis. J Craniomaxillofac Surg 2015; 43:528-36. [PMID: 25792443 DOI: 10.1016/j.jcms.2015.02.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [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: 01/01/2015] [Accepted: 02/06/2015] [Indexed: 10/24/2022] Open
Abstract
UNLABELLED Crouzon and Pfeiffer syndrome are syndromic craniosynostosis caused by specific mutations in the FGFR genes. Patients share the characteristics of a tall, flattened forehead, exorbitism, hypertelorism, maxillary hypoplasia and mandibular prognathism. Geometric morphometrics allows the identification of the global shape changes within and between the normal and syndromic population. METHODS Data from 27 Crouzon-Pfeiffer and 33 normal subjects were landmarked in order to compare both populations. With principal component analysis the variation within both groups was visualized and the vector of change was calculated. This model normalized a Crouzon-Pfeiffer skull and was compared to age-matched normative control data. RESULTS PCA defined a vector that described the shape changes between both populations. Movies showed how the normal skull transformed into a Crouzon-Pfeiffer phenotype and vice versa. Comparing these results to established age-matched normal control data confirmed that our model could normalize a Crouzon-Pfeiffer skull. CONCLUSIONS PCA was able to describe deformities associated with Crouzon-Pfeiffer syndrome and is a promising method to analyse variability in syndromic craniosynostosis. The virtual normalization of a Crouzon-Pfeiffer skull is useful to delineate the phenotypic changes required for correction, can help surgeons plan reconstructive surgery and is a potentially promising surgical outcome measure.
Collapse
Affiliation(s)
| | | | | | - Clifford Ruff
- Medical Physics Department, University College London, London, United Kingdom
| | - Maarten J Koudstaal
- Great Ormond Street Hospital, London, United Kingdom; Erasmus Medical Center, Maxillofacial Surgery, Rotterdam, The Netherlands
| | - David Dunaway
- Great Ormond Street Hospital, London, United Kingdom
| |
Collapse
|