| 1. |
- Eichhorst, B., et al.
(författare)
-
First-Line Venetoclax Combinations in Chronic Lymphocytic Leukemia.
- 2023
-
Ingår i: New England Journal of Medicine. - : MASSACHUSETTS MEDICAL SOC. - 0028-4793 .- 1533-4406. ; 388:19, s. 1739-1754
-
Tidskriftsartikel (refereegranskat)abstract
- Background Randomized trials of venetoclax plus anti-CD20 antibodies as first-line treatment in fit patients (i.e., those with a low burden of coexisting conditions) with advanced chronic lymphocytic leukemia (CLL) have been lacking. Methods In a phase 3, open-label trial, we randomly assigned, in a 1:1:1:1 ratio, fit patients with CLL who did not have TP53 aberrations to receive six cycles of chemoimmunotherapy (fludarabine-cyclophosphamide-rituximab or bendamustine-rituximab) or 12 cycles of venetoclax-rituximab, venetoclax-obinutuzumab, or venetoclax-obinutuzumab-ibrutinib. Ibrutinib was discontinued after two consecutive measurements of undetectable minimal residual disease or could be extended. The primary end points were undetectable minimal residual disease (sensitivity,
|
|
| 2. |
- Häggström, Ida, 1982, et al.
(författare)
-
Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
- 2024
-
Ingår i: The Lancet Digital Health. - 2589-7500. ; 6:2, s. e114-e125
-
Tidskriftsartikel (refereegranskat)abstract
- Background : The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. Methods : In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1–3 vs 4–5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. Findings : In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942–0·956), accuracy of 0·890 (0·879–0·901), sensitivity of 0·868 (0·851–0·885), and specificity of 0·913 (0·899–0·925); LARS-max achieved an AUC of 0·949 (0·942–0·956), accuracy of 0·868 (0·858–0·879), sensitivity of 0·909 (0·896–0·924), and specificity of 0·826 (0·808–0·843); and LARS-ptct achieved an AUC of 0·939 (0·930–0·948), accuracy of 0·875 (0·864–0·887), sensitivity of 0·836 (0·817–0·855), and specificity of 0·915 (0·901–0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938–0·966), accuracy of 0·907 (0·888–0·925), sensitivity of 0·874 (0·843–0·904), and specificity of 0·949 (0·921–0·960); LARS-max achieved an AUC of 0·952 (0·937–0·965), accuracy of 0·898 (0·878–0·916), sensitivity of 0·899 (0·871–0·926), and specificity of 0·897 (0·871–0·922); and LARS-ptct achieved an AUC of 0·932 (0·915–0·948), accuracy of 0·870 (0·850–0·891), sensitivity of 0·827 (0·793–0·863), and specificity of 0·913 (0·889–0·937). Interpretation : Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Funding: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.
|
|
| 3. |
- Zhang, Hong, et al.
(författare)
-
Sox4 Is a Key Oncogenic Target in C/EBP alpha Mutant Acute Myeloid Leukemia
- 2013
-
Ingår i: Cancer Cell. - : Elsevier BV. - 1878-3686 .- 1535-6108. ; 24:5, s. 575-588
-
Tidskriftsartikel (refereegranskat)abstract
- Mutation or epigenetic silencing of the transcription factor C/EBP alpha is observed in similar to 10% of patients with acute myeloid leukemia (AML). In both cases, a common global gene expression profile is observed, but downstream targets relevant for leukemogenesis are not known. Here, we identify Sox4 as a direct target of C/EBP alpha whereby its expression is inversely correlated with C/EBP alpha activity. Downregulation of Sox4 abrogated increased self-renewal of leukemic cells and restored their differentiation. Gene expression profiles of leukemia-initiating cells (LICs) from both Sox4 overexpression and murine C/EBP alpha mutant AML models clustered together but differed from other types of AML. Our data demonstrate that Sox4 overexpression resulting from C/EBP alpha inactivation contributes to the development of leukemia with a distinct LIC phenotype.
|
|
