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- Belavy, D. L., et al.
(författare)
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Characterization of Intervertebral Disc Changes in Asymptomatic Individuals with Distinct Physical Activity Histories Using Three Different Quantitative MRI Techniques
- 2020
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Ingår i: Journal of Clinical Medicine. - : MDPI AG. - 2077-0383. ; 9:6
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Tidskriftsartikel (refereegranskat)abstract
- (1) Background: Assessments of intervertebral disc (IVD) changes, and IVD tissue adaptations due to physical activity, for example, remains challenging. Newer magnetic resonance imaging techniques can quantify detailed features of the IVD, where T2-mapping and T2-weighted (T2w) and Dixon imaging are potential candidates. Yet, their relative utility has not been examined. The performances of these techniques were investigated to characterize IVD differences in asymptomatic individuals with distinct physical activity histories. (2) Methods: In total, 101 participants (54 women) aged 25-35 years with distinct physical activity histories but without histories of spinal disease were included. T11/12 to L5/S1 IVDs were examined with sagittal T2-mapping, T2w and Dixon imaging. (3) Results: T2-mapping differentiated Pfirrmann grade-1 from all other grades (p< 0.001). Most importantly, T2-mapping was able to characterize IVD differences in individuals with different training histories (p< 0.005). Dixon displayed weak correlations with the Pfirrmann scale, but presented significantly higher water content in the IVDs of the long-distance runners (p< 0.005). (4) Conclusions: Findings suggested that T2-mapping best reflects IVD differences in asymptomatic individuals with distinct physical activity histories changes. Dixon characterized new aspects of IVD, probably associated with IVD hypertrophy. This complementary information may help us to better understand the biological function of the disc.
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- Hebelka, Hanna, 1977, et al.
(författare)
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The importance of level stratification for quantitative MR studies of lumbar intervertebral discs: a cross-sectional analysis in 101 healthy adults
- 2019
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Ingår i: European Spine Journal. - : Springer Science and Business Media LLC. - 0940-6719 .- 1432-0932. ; 28:9, s. 2153-2161
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To investigate whether quantitative T2-times depend on lumbar intervertebral disc (IVD) level. Methods: The lumbar spine (Th12/L1–L5/S1) of 101 participants (53.5% female, 30.0[± 3.6]years, 173.5[± 9.6]cm and 69.9[± 13.4]kg), without history of back pain, was examined on a 3T scanner with sagittal T2-mapping. All IVDs were stratified according to Pfirrmann grade and lumbar level, with mean T2-time determined for the entire IVD volume and in five subregions of interests. Results: Significant level-dependent T2-time differences were detected, both for the entire IVD volume and its subregions. For the entire IVD volume, Pfirrmann grade 2 IVDs displayed 9–18% higher T2-times in Th12/L1 IVDs compared to L2/L3–L5/S1 IVDs (0.001 > p < 0.004) and significantly different T2-times in L1/L2–L2/L3 IVDs compared to most of the IVDs in the lower lumbar spine. In Pfirrmann grades 1, 3 and 4 IVDs, no significant level-dependent T2-time differences were observed for the entire IVD. More pronounced results were observed when comparing IVD subregions, with significant level-dependent differences also within Pfirrmann grade 1 and grade 3 IVDs. For example, in posterior IVD subregions mean T2-time was 80–82% higher in Th12/L1 compared to L3/L4–L4/L5 Pfirrmann grade 1 IVDs (p < 0.05) and 10–14% higher in L5/S1 compared to L3/L4–L4/L5 Pfirrmann grade 3 IVDs (0.02 > p < 0.001). Discussion: Significant level-dependent T2-time differences within several Pfirrmann grades, both for the entire IVD volume and for multiple IVD subregions, were shown in this large cohort study. The T2-time differences between levels existed in both non-degenerated and degenerated IVDs. These findings show the importance of stratifying for lumbar level when quantitative IVD studies are performed using T2-mapping. Graphic abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]. © 2019, The Author(s).
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- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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- Wang, M., et al.
(författare)
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Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling
- 2017
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Ingår i: Leukemia. - : NATURE PUBLISHING GROUP. - 0887-6924 .- 1476-5551. ; 31:10, s. 2029-2036
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Tidskriftsartikel (refereegranskat)abstract
- Risk stratification of acute myeloid leukemia (AML) patients needs improvement. Several AML risk classification models based on somatic mutations or gene-expression profiling have been proposed. However, systematic and independent validation of these models is required for future clinical implementation. We performed whole-transcriptome RNA-sequencing and panel-based deep DNA sequencing of 23 genes in 274 intensively treated AML patients (Clinseq-AML). We also utilized the The Cancer Genome Atlas (TCGA)-AML study (N = 142) as a second validation cohort. We evaluated six previously proposed molecular-based models for AML risk stratification and two revised risk classification systems combining molecular-and clinical data. Risk groups stratified by five out of six models showed different overall survival in cytogenetic normal-AML patients in the Clinseq-AML cohort (P-value < 0.05; concordance index > 40.5). Risk classification systems integrating mutational or gene-expression data were found to add prognostic value to the current European Leukemia Net (ELN) risk classification. The prognostic value varied between models and across cohorts, highlighting the importance of independent validation to establish evidence of efficacy and general applicability. All but one model replicated in the Clinseq-AML cohort, indicating the potential for molecular-based AML risk models. Risk classification based on a combination of molecular and clinical data holds promise for improved AML patient stratification in the future.
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