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Davey MS, Davey MG, Kenny P, Gheiti AJC. The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee-a systematic review and meta-analysis. Ir J Med Sci 2024:10.1007/s11845-024-03714-5. [PMID: 38822185 DOI: 10.1007/s11845-024-03714-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/14/2024] [Indexed: 06/02/2024]
Abstract
The primary aim of this study was to systematically review current literature evaluating the use of radiomics in establishing the role of magnetic resonance imaging (MRI) findings in native knees in predicting features of osteoarthritis (OA). A systematic review was performed with respect to PRISMA guidelines in search of studies reporting radiomic analysis of magnetic resonance imaging (MRI) to analyse patients with native knee OA. Sensitivity and specificity of radiomic analyses were included for meta-analysis. Following our initial literature search of 1271 studies, only 5 studies met our inclusion criteria. This included 1730 patients (71.5% females) with a mean age of 55.4 ± 15.6 years (range 24-66). The mean RQS of included studies was 16.6 (11-21). Meta-analysis demonstrated the pooled sensitivity and specificity for MRI in predicting features of OA in patients with native knees were 0.74 (95% CI 0.71, 0.78) and 0.85 (95% CI 0.83, 0.87), respectively. The results of this systematic review suggest that the high sensitivities and specificity of MRI-based radiomics may represent potential biomarker in the early identification and classification of native knee OA. Such analysis may inform surgeons to facilitate earlier non-operative management of knee OA in the select pre-symptomatic patients, prior to clinical or radiological evidence of degenerative change.
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Affiliation(s)
- Martin S Davey
- Connolly Hospital Blanchardstown, Dublin, Ireland.
- National Orthopaedic Hospital Cappagh, Dublin, Ireland.
- Royal College of Surgeons in Ireland, Dublin, Ireland.
| | | | - Paddy Kenny
- Connolly Hospital Blanchardstown, Dublin, Ireland
- National Orthopaedic Hospital Cappagh, Dublin, Ireland
- Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Adrian J Cassar Gheiti
- Connolly Hospital Blanchardstown, Dublin, Ireland
- National Orthopaedic Hospital Cappagh, Dublin, Ireland
- Royal College of Surgeons in Ireland, Dublin, Ireland
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Jia LL, Lei J. Response to letter by Fusco Roberta & Vincenza Granata-Re: Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis". Eur J Radiol 2023; 175:111195. [PMID: 38669754 DOI: 10.1016/j.ejrad.2023.111195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou City, Gansu Province, China.
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP, Huang G. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1026216. [PMID: 36313696 PMCID: PMC9597310 DOI: 10.3389/fonc.2022.1026216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.
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Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Qing-Yong Zheng
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jin-Hui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Di-Liang He
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Jian-Xin Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Lian-Ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Gang Huang,
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Identification of a Five-MiRNA Expression Assay to Aid Colorectal Cancer Diagnosis. GASTROINTESTINAL DISORDERS 2022. [DOI: 10.3390/gidisord4030018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Introduction: One-third of colorectal cancer (CRC) patients present with advanced disease, and establishing control remains a challenge. Identifying novel biomarkers to facilitate earlier diagnosis is imperative in enhancing oncological outcomes. We aimed to create miRNA oncogenic signature to aid CRC diagnosis. Methods: Tumour and tumour-associated normal (TAN) were extracted from 74 patients during surgery for CRC. RNA was isolated and target miRNAs were quantified using real-time reverse transcriptase polymerase chain reaction. Regression analyses were performed in order to identify miRNA targets capable of differentiating CRC from TAN and compared with two endogenous controls (miR-16 and miR-345) in each sample. Areas under the curve (AUCs) in Receiver Operating Characteristic (ROC) analyses were determined. Results: MiR-21 (β-coefficient:3.661, SE:1.720, p = 0.033), miR-31 (β-coefficient:2.783, SE:0.918, p = 0.002), and miR-150 (β-coefficient:−4.404, SE:0.526, p = 0.004) expression profiles differentiated CRC from TAN. In multivariable analyses, increased miR-31 (β-coefficient:2.431, SE:0.715, p < 0.001) and reduced miR-150 (β-coefficient:−4.620, SE:1.319, p < 0.001) independently differentiated CRC from TAN. The highest AUC generated for miR-21, miR-31, and miR-150 in an oncogenic expression assay was 83.0% (95%CI: 61.7–100.0, p < 0.001). In the circulation of 34 independent CRC patients and 5 controls, the mean expression of miR-21 (p = 0.001), miR-31 (p = 0.001), and miR-150 (p < 0.001) differentiated CRC from controls; however, the median expression of miR-21 (p = 0.476), miR-31 (p = 0.933), and miR-150 (p = 0.148) failed to differentiate these groups. Conclusion: This study identified a five-miRNA signature capable of distinguishing CRC from normal tissues with a high diagnostic test accuracy. Further experimentation with this signature is required to elucidate its diagnostic relevance in the circulation of CRC patients.
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Davey MG, Joyce WP. Impact of frailty on oncological outcomes in patients undergoing surgery for colorectal cancer - A systematic review and meta-analysis. Surgeon 2022; 21:173-180. [PMID: 35792005 DOI: 10.1016/j.surge.2022.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/09/2022] [Accepted: 06/14/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Frailty describes patients who are at an extreme risk of vulnerability to stressors that may lead to adverse clinical outcomes. The impact of frailty on clinical, oncological and survival outcomes in colorectal cancer (CRC) remains unclear. AIM To determine the anticipated oncological and survival outcomes for patients who are frail when diagnosed and undergo treatment with curative intent for CRC. METHODS A systematic review and meta-analysis was performed as per PRISMA guidelines. Descriptive statistics were used to determine associations between frailty and survival outcomes. The impact of frailty on disease-free and overall survival were expressed as hazard Ratios (HRs) and 95% confidence intervals (CIs) were estimated using the time-to-effect generic inverse variance and Mantel-Haenszel method. RESULTS Nine studies including 15,555 patients were included, of whom 8.1% were frail (1206/14,831). The mean age was 77.1 years (range: 42-94 years), 61.1% were female (9510/15,555) and mean follow-up was 48.0 months. Overall, frailty was associated with an increased risk of mortality (HR: 2.95, 95% CI: 1.64-5.29, P < 0.001) and worse disease-free survival (HR: 1.80, 95% CI: 1.34-2.41, P < 0.001). Frailty was also associated with an increased risk of mortality at 1-year (HR: 3.70, 95% CI: 1.00-13.66, P = 0.050) and 5-years (HR: 2.79, 95% CI: 1.65-4.71, P < 0.001) follow-up respectively. CONCLUSION Frailty is associated with poorer oncological and survival outcomes in patients diagnosed and treated with curative intent for CRC. CRC multidisciplinary team meetings should incorporate these findings into the management paradigm for these patients and patient counselling should be tailored to include these findings.
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Affiliation(s)
- Matthew G Davey
- Department of Surgery, Galway Clinic, Co. Galway H91 HHT0, Ireland; Royal College of Surgeons Ireland, 123 St. Stephens Green, Dublin 2, D02 YN77, Ireland.
| | - William P Joyce
- Department of Surgery, Galway Clinic, Co. Galway H91 HHT0, Ireland; Royal College of Surgeons Ireland, 123 St. Stephens Green, Dublin 2, D02 YN77, Ireland
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An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer. BMC Med Imaging 2022; 22:84. [PMID: 35538520 PMCID: PMC9087958 DOI: 10.1186/s12880-022-00813-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00813-6.
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Davey MG, Feeney G, Annuk H, Paganga M, Holian E, Lowery AJ, Kerin MJ, Miller N. MicroRNA Expression Profiling Predicts Nodal Status and Disease Recurrence in Patients Treated with Curative Intent for Colorectal Cancer. Cancers (Basel) 2022; 14:cancers14092109. [PMID: 35565239 PMCID: PMC9106021 DOI: 10.3390/cancers14092109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Approximately one-third of colorectal cancer (CRC) patients will suffer recurrence. MiRNAs are small non-coding RNAs that play important roles in gene expression. We aimed to correlate miRNA expression with aggressive clinicopathological characteristics and survival outcomes in CRC. Methods: Tumour samples were extracted from 74 CRC patients. MiRNAs were quantified using real-time reverse transcriptase polymerase chain reaction. Descriptive statistics and Cox regression analyses were performed to correlate miRNA targets with clinicopathological and outcome data. Results: Aberrant miR-21 and miR-135b expression correlate with increased nodal stage (p = 0.039, p = 0.022). Using univariable Cox regression analyses, reduced miR-135b (β-coefficient −1.126, hazard ratio 0.324, standard error (SE) 0.4698, p = 0.017) and increased miR-195 (β-coefficient 1.442, hazard ratio 4.229, SE 0.446, p = 0.001) predicted time to disease recurrence. Survival regression trees analysis illustrated a relative cut-off of ≤0.488 for miR-195 and a relative cut-off of >−0.218 for miR-135b; both were associated with improved disease recurrence (p < 0.001, p = 0.015). Using multivariable analysis with all targets as predictors, miR-195 (β-coefficient 3.187, SE 1.419, p = 0.025) was the sole significant independent predictor of recurrence. Conclusion: MiR-195 has strong value in predicting time to recurrence in CRC patients. Additionally, miR-21 and miR-135b predict the degree nodal burden. Future studies may include these findings to personalize therapeutic and surgical decision making.
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Affiliation(s)
- Matthew G. Davey
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
- Correspondence:
| | - Gerard Feeney
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
| | - Heidi Annuk
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
| | - Maxwell Paganga
- School of Mathematical and Statistical Sciences, National University of Ireland, H91 H3CY Galway, Ireland; (M.P.); (E.H.)
| | - Emma Holian
- School of Mathematical and Statistical Sciences, National University of Ireland, H91 H3CY Galway, Ireland; (M.P.); (E.H.)
| | - Aoife J. Lowery
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
| | - Michael J. Kerin
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
| | - Nicola Miller
- Department of Surgery, Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (G.F.); (H.A.); (A.J.L.); (M.J.K.); (N.M.)
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