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- Gerdtsson, Axel, et al.
(författare)
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Validation of a prediction model for post-chemotherapy fibrosis in nonseminoma patients
- 2023
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Ingår i: Bju International. - 1464-4096 .- 1464-410X. ; 132:3, s. 329-336
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Tidskriftsartikel (refereegranskat)abstract
- Objective To validate Vergouwe's prediction model using the Swedish and Norwegian Testicular Cancer Group (SWENOTECA) RETROP database and to define its clinical utility. Materials and methods Vergouwe's prediction model for benign histopathology in post-chemotherapy retroperitoneal lymph node dissection (PCRPLND) uses the following variables: presence of teratoma in orchiectomy specimen; pre-chemotherapy level of alphafetoprotein; b-Human chorionic gonadotropin and lactate dehydrogenase; and lymph node size pre- and postchemotherapy. Our validation cohort consisted of patients included in RETROP, a prospective population-based database of patients in Sweden and Norway with metastatic nonseminoma, who underwent PC-RPLND in the period 2007-2014. Discrimination and calibration analyses were used to validate Vergouwe's prediction model results. Calibration plots were created and a Hosmer-Lemeshow test was calculated. Clinical utility, expressed as opt-out net benefit (NBopt-out), was analysed using decision curve analysis. Results Overall, 284 patients were included in the analysis, of whom 130 (46%) had benign histology after PC-RPLND. Discrimination analysis showed good reproducibility, with an area under the receiver-operating characteristic curve (AUC) of 0.82 (95% confidence interval 0.77-0.87) compared to Vergouwe's prediction model (AUC between 0.77 and 0.84). Calibration was acceptable with no recalibration. Using a prediction threshold of 70% for benign histopathology, NBopt-out was 0.098. Using the model and this threshold, 61 patients would have been spared surgery. However, only 51 of 61 were correctly classified as benign. Conclusions The model was externally validated with good reproducibility. In a clinical setting, the model may identify patients with a high chance of benign histopathology, thereby sparing patients of surgery. However, meticulous follow-up is required.
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- Gerdtsson, Anna S, et al.
(författare)
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Evaluation of Solid Supports for Slide- and Well-Based Recombinant Antibody Microarrays
- 2016
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Ingår i: Microarrays. - : MDPI AG. - 2076-3905. ; 5:2
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Tidskriftsartikel (refereegranskat)abstract
- Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the antibodies and the solid surfaces plays a central role. In this study, we have taken on the first comprehensive view and evaluated the overall impact of solid surfaces on the recombinant antibody microarray design. The results clearly demonstrated the importance of the surface-antibody interaction and showed the effect of the solid supports on the printing process, the array format of planar arrays (slide- and well-based), the assay performance (spot features, reproducibility, specificity and sensitivity) and assay processing (degree of automation). In the end, two high-end recombinant antibody microarray technology platforms were designed, based on slide-based (black polymer) and well-based (clear polymer) arrays, paving the way for future large-scale protein expression profiling efforts.
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- Gerdtsson, Anna Sandström, et al.
(författare)
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Plasma protein profiling in a stage defined pancreatic cancer cohort – Implications for early diagnosis
- 2016
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Ingår i: Molecular Oncology. - : Wiley. - 1574-7891. ; 10:8, s. 1305-1316
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Tidskriftsartikel (refereegranskat)abstract
- Pancreatic ductal adenocarcinoma (PDAC) is a disease where detection preceding clinical symptoms significantly increases the life expectancy of patients. In this study, a recombinant antibody microarray platform was used to analyze 213 Chinese plasma samples from PDAC patients and normal control (NC) individuals. The cohort was stratified according to disease stage, i.e. resectable disease (stage I/II), locally advanced (stage III) and metastatic disease (stage IV). Support vector machine analysis showed that all PDAC stages could be discriminated from controls and that the accuracy increased with disease progression, from stage I to IV. Patients with stage I/II PDAC could be discriminated from NC with high accuracy based on a plasma protein signature, indicating a possibility for early diagnosis and increased detection rate of surgically resectable tumors.
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