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» Authors » Ellen G Engelhardt

Ellen G Engelhardt

Explore the profile of Ellen G Engelhardt including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 31
Citations 380
Followers 0
Related Specialties
Oncology
General Medicine
Gynecology & Obstetrics
Top 10 Co-Authors
Anne M Stiggelbout
Ellen M A Smets
Arwen H Pieterse
Eveline M A Bleiker
Marjanka K Schmidt
Johanneke E A Portielje
Judith R Kroep
Jelle Wesseling
Hanneke C J M de Haes
Frederieke H van Duijnhoven
Published In
Cancers (Basel)
Eur J Cancer
Acta Oncol
Med Decis Making
BMC Med Inform Decis Mak
Affiliations
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Recent Articles
1.
Patients' and plastic surgeons' experiences with an online patient decision aid for breast reconstruction: considerations for nationwide implementation
Stege J, Engelhardt E, Woerdeman L, Oldenburg H, Kieffer J, Hahn D, et al.
BMC Med Inform Decis Mak . 2025 Feb; 25(1):62. PMID: 39915836
Background: Women diagnosed with breast cancer undergoing a mastectomy often have the option to undergo breast reconstruction (BR). BR decisions are complex and have considerable impact. We developed a patient...
2.
Risk-Prediction Models for Clinical Decision-Making in Sarcoma Care: An International Survey Among Soft-Tissue Sarcoma Clinicians
Kruiswijk A, Vlug L, Acem I, Engelhardt E, Gronchi A, Callegaro D, et al.
Ann Surg Oncol . 2025 Feb; 32(4):2958-2970. PMID: 39893340
Introduction: Risk prediction models (RPMs) are statistical tools that predict outcomes on the basis of clinical characteristics and can thereby support (shared) decision-making. With the shift toward personalized medicine, the...
3.
Understanding how a personalized risk prediction tool (VALUE-PERSARC) supports informed treatment decisions of soft-tissue sarcomas patients in daily clinical practice - A mixed methods study
Kruiswijk A, Engelhardt E, Vlug L, van de Wal R, Schrage Y, Haas R, et al.
Eur J Cancer . 2024 Sep; 210:114269. PMID: 39226665
Introduction: Risk prediction models (RPM) can help soft-tissue sarcoma(STS) patients and clinicians make informed treatment decisions by providing them with estimates of (disease-free) survival for different treatment options. However, it...
4.
Presenting decision-relevant numerical information to Dutch women aged 50-70 with varying levels of health literacy: Case example of adjuvant systemic therapy for breast cancer
van Strien-Knippenberg I, Timmermans D, Engelhardt E, Konings I, Damman O
PLoS One . 2024 Sep; 19(9):e0309668. PMID: 39226280
Background: If communicated adequately, numerical decision-relevant information can support informed and shared decision making. Visual formats are recommended, but which format supports patients depending on their health literacy (HL) levels...
5.
Information and communication priorities of patients and healthcare professionals in shared decision making regarding adjuvant systemic breast cancer treatment: A survey study
Damman O, van Strien-Knippenberg I, Engelhardt E, Determann D D, de Bruijne M, Siesling S, et al.
Eur J Oncol Nurs . 2024 Apr; 70:102574. PMID: 38643680
Purpose: To assess information and communication priorities of patients and healthcare professionals in Shared Decision Making about adjuvant systemic treatment of primary breast cancer and identify key decision-relevant information accordingly....
6.
Evaluation of the Implementation of the Dutch Breast Cancer Surveillance Decision Aid including Personalized Risk Estimates in the SHOUT-BC Study: A Mixed Methods Approach
Ankersmid J, Engelhardt E, Lansink Rotgerink F, The R, Strobbe L, Drossaert C, et al.
Cancers (Basel) . 2024 Apr; 16(7). PMID: 38611068
Background: To improve Shared decision-making (SDM) regarding personalized post-treatment surveillance, the Breast Cancer Surveillance Decision Aid (BCS-PtDA), integrating personalized risk information, was developed and implemented in eight hospitals. The aim...
7.
(Cost-)effectiveness of an individualised risk prediction tool (PERSARC) on patient's knowledge and decisional conflict among soft-tissue sarcomas patients: protocol for a parallel cluster randomised trial (the VALUE-PERSARC study)
Kruiswijk A, van de Sande M, Haas R, van den Akker-van Marle E, Engelhardt E, Marang-van de Mheen P, et al.
BMJ Open . 2023 Nov; 13(11):e074853. PMID: 37918933
Introduction: Current treatment decision-making in high-grade soft-tissue sarcoma (STS) care is not informed by individualised risks for different treatment options and patients' preferences. Risk prediction tools may provide patients and...
8.
Active surveillance versus treatment in low-risk DCIS: Women's preferences in the LORD-trial
Schmitz R, Engelhardt E, Gerritsma M, Sondermeijer C, Verschuur E, Houtzager J, et al.
Eur J Cancer . 2023 Sep; 192:113276. PMID: 37657228
Background: Ductal carcinoma in situ (DCIS) can progress to invasive breast cancer (IBC), but most DCIS lesions remain indolent. However, guidelines recommend surgery, often supplemented by radiotherapy. This implies overtreatment...
9.
De-ESCAlating RadioTherapy in breast cancer patients with pathologic complete response to neoadjuvant systemic therapy: DESCARTES study
van Hemert A, van Olmen J, Boersma L, Maduro J, Russell N, Tol J, et al.
Breast Cancer Res Treat . 2023 Mar; 199(1):81-89. PMID: 36892723
Purpose: Neoadjuvant systemic therapy (NST) is increasingly used in breast cancer patients and depending on subtype, 10-89% of patients will attain pathologic complete response (pCR). In patients with pCR, risk...
10.
Psychological impact of referral to an oncology hospital on patients with an ovarian mass
Lof P, Engelhardt E, van Gent M, Mom C, Rosier-van Dunne F, van Baal W, et al.
Int J Gynecol Cancer . 2023 Jan; 33(1):74-82. PMID: 36600495
Objectives: In patients with an ovarian mass, a risk of malignancy assessment is used to decide whether referral to an oncology hospital is indicated. Risk assessment strategies do not perform...