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Artikel ; Online: CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology.

Flach, Rachel N / Stathonikos, Nikolas / Nguyen, Tri Q / Ter Hoeve, Natalie D / van Diest, Paul J / van Dooijeweert, Carmen

BMJ open

2023 Band 13, Heft 6, Seite(n) e067437

Abstract: Introduction: Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI have ... ...

Abstract	Introduction: Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI have yet to be performed, to the best of our knowledge. In this trial, we will explore the benefits of an AI-assisted pathology workflow, while maintaining diagnostic safety standards. Methods and analysis: This is a Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence compliant single-centre, controlled clinical trial, in a fully digital academic pathology laboratory. We will prospectively include prostate cancer patients who undergo prostate needle biopsies (CONFIDENT-P) and breast cancer patients who undergo a sentinel node procedure (CONFIDENT-B) in the University Medical Centre Utrecht. For both the CONFIDENT-B and CONFIDENT-P trials, the specific pathology specimens will be pseudo-randomised to be assessed by a pathologist with or without AI assistance in a pragmatic (bi-)weekly sequential design. In the intervention group, pathologists will assess whole slide images (WSI) of the standard hematoxylin and eosin (H&E)-stained sections assisted by the output of the algorithm. In the control group, pathologists will assess H&E WSI according to the current clinical workflow. If no tumour cells are identified or when the pathologist is in doubt, immunohistochemistry (IHC) staining will be performed. At least 80 patients in the CONFIDENT-P and 180 patients in the CONFIDENT-B trial will need to be enrolled to detect superiority, allocated as 1:1. Primary endpoint for both trials is the number of saved resources of IHC staining procedures for detecting tumour cells, since this will clarify tangible cost savings that will support the business case for AI. Ethics and dissemination: The ethics committee (MREC NedMec) waived the need of official ethical approval, since participants are not subjected to procedures nor are they required to follow rules. Results of both trials (CONFIDENT-B and CONFIDENT-P) will be published in scientific peer-reviewed journals.
Mesh-Begriff(e)	Male ; Humans ; Artificial Intelligence ; Prospective Studies ; Retrospective Studies ; Breast Neoplasms/diagnosis ; Breast Neoplasms/pathology ; Algorithms
Sprache	Englisch
Erscheinungsdatum	2023-06-07
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2022-067437
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow.

van Bergeijk, Stijn A / Stathonikos, Nikolas / Ter Hoeve, Natalie D / Lafarge, Maxime W / Nguyen, Tri Q / van Diest, Paul J / Veta, Mitko

Journal of pathology informatics

2023 Band 14, Seite(n) 100316

Abstract: Introduction: Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to ... ...

Abstract	Introduction: Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations. Methods: Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ. Results: MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R Conclusion: This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.
Sprache	Englisch
Erscheinungsdatum	2023-05-04
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2023.100316
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Time Trends in Histopathological Findings in Mammaplasty Specimens in a Dutch Academic Pathology Laboratory.

Stutterheim, Hannah W / Ter Hoeve, Natalie D / Maarse, Wiesje / van der Wall, Elsken / van Diest, Paul J

Plastic and reconstructive surgery. Global open

2023 Band 11, Heft 6, Seite(n) e4966

Abstract: Reduction mammaplasties are often performed at a relatively young age. Necessity of routine pathological investigation of the removed breast tissue to exclude breast cancer has been debated. Past studies have shown 0.05%-4.5% significant findings in ... ...

Abstract	Reduction mammaplasties are often performed at a relatively young age. Necessity of routine pathological investigation of the removed breast tissue to exclude breast cancer has been debated. Past studies have shown 0.05%-4.5% significant findings in reduction specimens, leading to an ongoing debate whether this is cost-effective. There is also no current Dutch guideline on pathological investigation of mammaplasty specimens. Because the incidence of breast cancer is rising, especially among young women, we re-evaluated the yield of routine pathological investigation of mammaplasty specimens over three decades in search of time trends. Methods: Reduction specimens from 3430 female patients examined from 1988 to 2021 in the UMC Utrecht were evaluated. Significant findings were defined as those that may lead to more intensive follow-up or surgical intervention. Results: Mean age of patients was 39 years. Of the specimens, 67.4% were normal; 28.9% displayed benign changes; 2.7%, benign tumors; 0.3%, premalignant changes; 0.8%, in situ; and 0.1%, invasive cancers. Most patients with significant findings were in their forties ( Conclusions: Over three decades, 1.2% of mammaplasty specimens displayed significant findings on routine pathology examination, with an incidence rising to 2.1% from 2016 onward. The main reason for this recent increase is probably attributable to super-specialization by the pathologists. While awaiting formal cost-effectiveness studies, the frequency of significant findings for now seems to justify routine pathological examination of mammaplasty reduction specimens.
Sprache	Englisch
Erscheinungsdatum	2023-06-22
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2851682-5
ISSN	2169-7574 ; 2169-7574
ISSN (online)	2169-7574
ISSN	2169-7574
DOI	10.1097/GOX.0000000000004966
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: PD-1 and PD-L1 Expression in Male Breast Cancer in Comparison with Female Breast Cancer.

Manson, Quirine F / Ter Hoeve, Natalie D / Buerger, Horst / Moelans, Cathy B / van Diest, Paul J

Targeted oncology

2018 Band 13, Heft 6, Seite(n) 769–777

Abstract: Background: Male breast cancer is rare, as it represents less than 1% of all breast cancer cases. In addition, male breast cancer appears to have a different biology than female breast cancer. Programmed death-1 (PD-1) and its ligand, programmed death- ... ...

Abstract	Background: Male breast cancer is rare, as it represents less than 1% of all breast cancer cases. In addition, male breast cancer appears to have a different biology than female breast cancer. Programmed death-1 (PD-1) and its ligand, programmed death-ligand 1 (PD-L1), seem to have prognostic and predictive values in a variety of cancers, including female breast cancer. However, the role of PD-1 and PD-L1 expression in male breast cancer has not yet been studied. Objectives: To compare PD-1 and PD-L1 expression in male breast cancer to female breast cancer and to evaluate prognostic values in both groups. Patients and methods: Tissue microarrays from formalin-fixed paraffin-embedded resection material of 247 female and 164 male breast cancer patients were stained for PD-1 and PD-L1 by immunohistochemistry. Results: PD-1 expression on tumor-infiltrating lymphocytes was significantly less frequent in male than in female cancers (48.9 vs. 65.3%, p = 0.002). In contrast, PD-L1 expression on tumor and immune cells did not differ between the two groups. In male breast cancer, PD-1 and tumor PD-L1 were associated with grade 3 tumors. In female breast cancer, PD-1 and PD-L1 were associated with comparably worse clinicopathological variables. In a survival analysis, no prognostic value was observed for PD-1 and PD-L1 in either male and female breast cancer. In a subgroup analysis, female patients with grade 3/tumor PD-L1-negative or ER-negative/immune PD-L1-negative tumors had worse overall survival. Conclusions: PD-1 seems to be less often expressed in male breast cancer compared to female breast cancer. Although PD-1 and PD-L1 are not definite indicators for good or bad responses, male breast cancer patients may therefore respond differently to checkpoint immunotherapy with PD-1 inhibitors than female patients.
Mesh-Begriff(e)	Adult ; Aged ; Aged, 80 and over ; B7-H1 Antigen/biosynthesis ; B7-H1 Antigen/immunology ; Biomarkers, Tumor/biosynthesis ; Biomarkers, Tumor/immunology ; Breast Neoplasms/immunology ; Breast Neoplasms/pathology ; Breast Neoplasms, Male/immunology ; Breast Neoplasms, Male/pathology ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; Programmed Cell Death 1 Receptor/biosynthesis ; Programmed Cell Death 1 Receptor/immunology ; Retrospective Studies ; Sex Factors
Chemische Substanzen	B7-H1 Antigen ; Biomarkers, Tumor ; CD274 protein, human ; PDCD1 protein, human ; Programmed Cell Death 1 Receptor
Sprache	Englisch
Erscheinungsdatum	2018-12-06
Erscheinungsland	France
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2222136-0
ISSN	1776-260X ; 1776-2596
ISSN (online)	1776-260X
ISSN	1776-2596
DOI	10.1007/s11523-018-0610-1
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Long-term outcomes of young, node-negative, chemotherapy-naïve, triple-negative breast cancer patients according to BRCA1 status.

Wang, Yuwei / Dackus, Gwen M H E / Rosenberg, Efraim H / Cornelissen, Sten / de Boo, Leonora W / Broeks, Annegien / Brugman, Wim / Chan, Terry W S / van Diest, Paul J / Hauptmann, Michael / Ter Hoeve, Natalie D / Isaeva, Olga I / de Jong, Vincent M T / Jóźwiak, Katarzyna / Kluin, Roelof J C / Kok, Marleen / Koop, Esther / Nederlof, Petra M / Opdam, Mark /

Schouten, Philip C / Siesling, Sabine / van Steenis, Charlaine / Voogd, Adri C / Vreuls, Willem / Salgado, Roberto F / Linn, Sabine C / Schmidt, Marjanka K

BMC medicine

2024 Band 22, Heft 1, Seite(n) 9

Abstract: Background: Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers ... ...

Abstract	Background: Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers modify the well-established prognostic value of stromal tumor-infiltrating lymphocytes (sTILs) is unknown. This study aimed to compare the outcomes of young, node-negative, chemotherapy-naïve TNBC patients according to BRCA1 status, taking sTILs into account. Methods: We included 485 Dutch women diagnosed with node-negative TNBC under age 40 between 1989 and 2000. During this period, these women were considered low-risk and did not receive chemotherapy. BRCA1 status, including pathogenic germline BRCA1 mutation (gBRCA1m), somatic BRCA1 mutation (sBRCA1m), and tumor BRCA1 promoter methylation (BRCA1-PM), was assessed using DNA from formalin-fixed paraffin-embedded tissue. sTILs were assessed according to the international guideline. Patients' outcomes were compared using Cox regression and competing risk models. Results: Among the 399 patients with BRCA1 status, 26.3% had a gBRCA1m, 5.3% had a sBRCA1m, 36.6% had tumor BRCA1-PM, and 31.8% had BRCA1-non-altered tumors. Compared to BRCA1-non-alteration, gBRCA1m was associated with worse overall survival (OS) from the fourth year after diagnosis (adjusted HR, 2.11; 95% CI, 1.18-3.75), and this association attenuated after adjustment for second primary tumors. Every 10% sTIL increment was associated with 16% higher OS (adjusted HR, 0.84; 95% CI, 0.78-0.90) in gBRCA1m, sBRCA1m, or BRCA1-non-altered patients and 31% higher OS in tumor BRCA1-PM patients. Among the 66 patients with tumor BRCA1-PM and ≥ 50% sTILs, we observed excellent 15-year OS (97.0%; 95% CI, 92.9-100%). Conversely, among the 61 patients with gBRCA1m and < 50% sTILs, we observed poor 15-year OS (50.8%; 95% CI, 39.7-65.0%). Furthermore, gBRCA1m was associated with higher (adjusted subdistribution HR, 4.04; 95% CI, 2.29-7.13) and tumor BRCA1-PM with lower (adjusted subdistribution HR, 0.42; 95% CI, 0.19-0.95) incidence of second primary tumors, compared to BRCA1-non-alteration. Conclusions: Although both gBRCA1m and tumor BRCA1-PM alter BRCA1 gene transcription, they are associated with different outcomes in young, node-negative, chemotherapy-naïve TNBC patients. By combining sTILs and BRCA1 status for risk classification, we were able to identify potential subgroups in this population to intensify and optimize adjuvant treatment.
Mesh-Begriff(e)	Humans ; Female ; Adult ; Triple Negative Breast Neoplasms/drug therapy ; Triple Negative Breast Neoplasms/genetics ; Neoplasms, Second Primary ; Adjuvants, Immunologic ; Ethnicity ; Biomarkers ; BRCA1 Protein/genetics
Chemische Substanzen	Adjuvants, Immunologic ; Biomarkers ; BRCA1 protein, human ; BRCA1 Protein
Sprache	Englisch
Erscheinungsdatum	2024-01-09
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2131669-7
ISSN	1741-7015 ; 1741-7015
ISSN (online)	1741-7015
ISSN	1741-7015
DOI	10.1186/s12916-023-03233-7
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.

Wetstein, Suzanne C / Stathonikos, Nikolas / Pluim, Josien P W / Heng, Yujing J / Ter Hoeve, Natalie D / Vreuls, Celien P H / van Diest, Paul J / Veta, Mitko

Laboratory investigation; a journal of technical methods and pathology

2021 Band 101, Heft 4, Seite(n) 525–533

Abstract: Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions ... ...

Abstract	Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κ
Mesh-Begriff(e)	Biopsy ; Breast/pathology ; Breast Neoplasms/diagnosis ; Breast Neoplasms/pathology ; Carcinoma, Intraductal, Noninfiltrating/diagnosis ; Carcinoma, Intraductal, Noninfiltrating/pathology ; Deep Learning ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Middle Aged ; Neoplasm Grading/methods
Sprache	Englisch
Erscheinungsdatum	2021-02-19
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80178-1
ISSN	1530-0307 ; 0023-6837
ISSN (online)	1530-0307
ISSN	0023-6837
DOI	10.1038/s41374-021-00540-6
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer.

Wang, Yuwei / Broeks, Annegien / Giardiello, Daniele / Hauptmann, Michael / Jóźwiak, Katarzyna / Koop, Esther A / Opdam, Mark / Siesling, Sabine / Sonke, Gabe S / Stathonikos, Nikolas / Ter Hoeve, Natalie D / van der Wall, Elsken / van Deurzen, Carolien H M / van Diest, Paul J / Voogd, Adri C / Vreuls, Willem / Linn, Sabine C / Dackus, Gwen M H E / Schmidt, Marjanka K

European journal of cancer (Oxford, England : 1990)

2023 Band 195, Seite(n) 113401

Abstract: Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast ... ...

Abstract	Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment. Methods: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds. Results: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC Conclusions: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.
Mesh-Begriff(e)	Humans ; Female ; Adult ; Prognosis ; Breast Neoplasms/pathology ; Chemotherapy, Adjuvant ; Registries ; Netherlands
Sprache	Englisch
Erscheinungsdatum	2023-10-30
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	82061-1
ISSN	1879-0852 ; 0277-5379 ; 0959-8049 ; 0964-1947
ISSN (online)	1879-0852
ISSN	0277-5379 ; 0959-8049 ; 0964-1947
DOI	10.1016/j.ejca.2023.113401
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Frequent discordance in PD-1 and PD-L1 expression between primary breast tumors and their matched distant metastases.

Manson, Quirine F / Schrijver, Willemijne A M E / Ter Hoeve, Natalie D / Moelans, Cathy B / van Diest, Paul J

Clinical & experimental metastasis

2018 Band 36, Heft 1, Seite(n) 29–37

Abstract: Programmed death-1 (PD-1) is an immune checkpoint that is able to inhibit the immune system by binding to its ligand programmed death-ligand 1 (PD-L1). In many cancer types, among which breast cancer, prognostic and/or predictive values have been ... ...

Abstract	Programmed death-1 (PD-1) is an immune checkpoint that is able to inhibit the immune system by binding to its ligand programmed death-ligand 1 (PD-L1). In many cancer types, among which breast cancer, prognostic and/or predictive values have been suggested for both PD-1 and PD-L1. Previous research has demonstrated discrepancies in PD-L1 expression between primary breast tumors and distant metastases, however data so far have been scarce. We therefore evaluated immunohistochemical expression levels of PD-1 and PD-L1 in primary breast tumors and their paired distant metastases, and evaluated prognostic values. Tissue microarrays from formalin-fixed paraffin-embedded resection specimens of primary breast cancers and their matched distant metastases were immunohistochemically stained for PD-1 and PD-L1. PD-1 was available in both primary tumor and metastasis in 82 patients, and PD-L1 in 49 patients. PD-1 was discrepant between primary tumor and metastasis in half of the patients (50%), PD-L1 on tumor cells was discrepant in 28.5%, and PD-L1 on immune cells in 40.8% of the patients. In primary tumors there was a correlation between PD-1 positivity and a higher tumor grade, and between immune PD-L1 and ER negativity. In survival analyses, a significantly better overall survival was observed for patients with PD-L1 negative primary breast tumors that developed PD-L1 positive distant metastases (HR 3.013, CI 1.201-7.561, p = 0.019). To conclude, PD-1 and tumor and immune PD-L1 seem to be discordantly expressed between primary tumors and their matched distant metastases in about one-third to a half of the breast cancer patients. Further, gained expression of PD-L1 in metastases seems to indicate better survival. This illustrates the need of reassessing PD-1 and PD-L1 expression on biopsies of distant metastases to optimize the usefulness of these biomarkers.
Mesh-Begriff(e)	Adult ; Aged ; Aged, 80 and over ; B7-H1 Antigen/metabolism ; Biomarkers, Tumor/metabolism ; Breast Neoplasms/metabolism ; Breast Neoplasms/pathology ; Carcinoma, Ductal, Breast/metabolism ; Carcinoma, Ductal, Breast/secondary ; Carcinoma, Lobular/metabolism ; Carcinoma, Lobular/secondary ; Female ; Follow-Up Studies ; Humans ; Lymphatic Metastasis ; Middle Aged ; Prognosis ; Programmed Cell Death 1 Receptor/metabolism ; Survival Rate
Chemische Substanzen	B7-H1 Antigen ; Biomarkers, Tumor ; CD274 protein, human ; PDCD1 protein, human ; Programmed Cell Death 1 Receptor
Sprache	Englisch
Erscheinungsdatum	2018-12-13
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	604952-7
ISSN	1573-7276 ; 0262-0898
ISSN (online)	1573-7276
ISSN	0262-0898
DOI	10.1007/s10585-018-9950-6
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: The effect of an e-learning module on grading variation of (pre)malignant breast lesions.

van Dooijeweert, Carmen / Deckers, Ivette A G / de Ruiter, Emma J / Ter Hoeve, Natalie D / Vreuls, Celien P H / van der Wall, Elsken / van Diest, Paul J

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

2020 Band 33, Heft 10, Seite(n) 1961–1967

Abstract: Histologic grade is a biomarker that is widely used to guide treatment of invasive breast cancer (IBC) and ductal carcinoma in situ of the breast (DCIS). Yet, currently, substantial grading variation between laboratories and pathologists exists in daily ... ...

Abstract	Histologic grade is a biomarker that is widely used to guide treatment of invasive breast cancer (IBC) and ductal carcinoma in situ of the breast (DCIS). Yet, currently, substantial grading variation between laboratories and pathologists exists in daily pathology practice. This study was conducted to evaluate whether an e-learning may be a feasible tool to decrease grading variation of (pre)malignant breast lesions. An e-learning module, representing the key-concepts of grading (pre)malignant breast lesions through gold standard digital images, was designed. Pathologists and residents could take part in either or both the separate modules on DCIS and IBC. Variation in grading of a digital set of lesions before and after the e-learning was compared in a fully-crossed study-design. Multiple outcome measures were assessed: inter-rater reliability (IRR) by Light's kappa, the number of images graded unanimously, the number of images with both extreme scores (i.e., grade I and grade III), and the average number of discrepancies from expert-consensus. Participants were included as they completed both the pre- and post-e-learning set (DCIS-module: n = 36, IBC-module: n = 21). For DCIS, all outcome measures improved after e-learning, with the IRR improving from fair (kappa: 0.532) to good (kappa: 0.657). For IBC, all outcome measures for the subcategories tubular differentiation and mitosis improved, with >90% of participants agreeing on almost 90% of the images after the e-learning. In contrast, the IRR for the subcategory of nuclear pleomorphism remained fair (kappa: 0.523 vs. kappa: 0.571). This study shows that an e-learning module, in which pathologists and residents are trained in histologic grading of DCIS and IBC, is a feasible and promising tool to decrease grading variation of (pre)malignant breast lesions. This is highly relevant given the important role of histologic grading in clinical decision making of (pre)malignant breast lesions.
Mesh-Begriff(e)	Breast Neoplasms/pathology ; Carcinoma, Intraductal, Noninfiltrating/pathology ; Computer-Assisted Instruction/methods ; Education, Medical/methods ; Female ; Humans ; Neoplasm Grading/methods ; Pathologists/education ; Pathology/education
Sprache	Englisch
Erscheinungsdatum	2020-05-13
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	645073-8
ISSN	1530-0285 ; 0893-3952
ISSN (online)	1530-0285
ISSN	0893-3952
DOI	10.1038/s41379-020-0556-6
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

Wetstein, Suzanne C. / Stathonikos, Nikolas / Pluim, Josien P. W. / Heng, Yujing J. / ter Hoeve, Natalie D. / Vreuls, Celien P. H. / van Diest, Paul J. / Veta, Mitko

2020

Abstract	Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading system. It was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the observers (o1, o2 and o3) ($\kappa_{o1,dl}=0.81, \kappa_{o2,dl}=0.53, \kappa_{o3,dl}=0.40$) than the observers amongst each other ($\kappa_{o1,o2}=0.58, \kappa_{o1,o3}=0.50, \kappa_{o2,o3}=0.42$) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers ($\kappa_{o1,dl}=0.77, \kappa_{o2,dl}=0.75, \kappa_{o3,dl}=0.70$) as the observers amongst each other ($\kappa_{o1,o2}=0.77, \kappa_{o1,o3}=0.75, \kappa_{o2,o3}=0.72$). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.
Schlagwörter	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	006
Erscheinungsdatum	2020-10-07
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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