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- Lindmark, Gudrun, et al.
(författare)
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qRT-PCR analysis of CEACAM5, KLK6, SLC35D3, MUC2 and POSTN in colon cancer lymph nodes : An improved method for assessment of tumor stage and prognosis
- 2024
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Ingår i: International Journal of Cancer. - : John Wiley & Sons. - 0020-7136 .- 1097-0215. ; 154:3, s. 573-584
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Tidskriftsartikel (refereegranskat)abstract
- One fourth of colorectal cancer patients having curative surgery will relapse of which the majority will die. Lymph node (LN) metastasis is the single most important prognostic factor and a key factor when deciding on postoperative treatment. Presently, LN metastases are identified by histopathological examination, a subjective method analyzing only a small LN volume and giving no information on tumor aggressiveness. To better identify patients at risk of relapse we constructed a qRT-PCR test, ColoNode, that determines levels of CEACAM5, KLK6, SLC35D3, MUC2 and POSTN mRNAs. Combined these biomarkers estimate the tumor cell load and aggressiveness allocating patients to risk categories with low (0, −1), medium (1), high (2) and very high (3) risk of recurrence. Here we present result of a prospective, national multicenter study including 196 colon cancer patients from 8 hospitals. On average, 21 LNs/patient, totally 4698 LNs, were examined by both histopathology and ColoNode. At 3-year follow-up, 36 patients had died from colon cancer or lived with recurrence. ColoNode identified all patients that were identified by histopathology and in addition 9 patients who were undetected by histopathology. Thus, 25% of the patients who recurred were identified by ColoNode only. Multivariate Cox regression analysis proved ColoNode (1, 2, 3 vs 0, −1) as a highly significant risk factor with HR 4.24 [95% confidence interval, 1.42-12.69, P =.01], while pTN-stage (III vs I/II) lost its univariate significance. In conclusion, ColoNode surpassed histopathology by identifying a significantly larger number of patients with future relapse and will be a valuable tool for decisions on postoperative treatment.
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| 3. |
- Wallden, Mats, et al.
(författare)
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Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care
- 2024
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Ingår i: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPredictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder.MethodsSince the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed.ResultsThe relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days.ConclusionsThe eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.
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