LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 75

Search options

  1. Article ; Online: Potential Biases in a Population-based Study of Surveillance Imaging for Head and Neck Cancer.

    Gensheimer, Michael F

    Radiology

    2023  Volume 308, Issue 2, Page(s) e230286

    MeSH term(s) Humans ; Diagnostic Imaging ; Head and Neck Neoplasms/diagnostic imaging ; Bias
    Language English
    Publishing date 2023-08-08
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.230286
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Ue I Zs.106: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Neck Dissection for Adenoid Cystic Carcinoma.

    Gensheimer, Michael F

    Annals of surgical oncology

    2020  Volume 27, Issue Suppl 3, Page(s) 925

    MeSH term(s) Carcinoma, Adenoid Cystic/surgery ; Head and Neck Neoplasms/surgery ; Humans ; Lymph Nodes ; Lymphatic Metastasis ; Neck Dissection
    Language English
    Publishing date 2020-04-20
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 1200469-8
    ISSN 1534-4681 ; 1068-9265
    ISSN (online) 1534-4681
    ISSN 1068-9265
    DOI 10.1245/s10434-020-08501-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 4042: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Lessons and Opportunities for Biomarker-Driven Radiation Personalization in Head and Neck Cancer.

    Rahimy, Elham / Gensheimer, Michael F / Beadle, Beth / Le, Quynh-Thu

    Seminars in radiation oncology

    2023  Volume 33, Issue 3, Page(s) 336–347

    Abstract: Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence ...

    Abstract Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence is often difficult to salvage with poor survival rates and functional morbidity.
    MeSH term(s) Humans ; Carcinoma, Squamous Cell/therapy ; Head and Neck Neoplasms/diagnostic imaging ; Head and Neck Neoplasms/radiotherapy ; Oropharyngeal Neoplasms/radiotherapy ; Prognosis ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-06-18
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1146999-7
    ISSN 1532-9461 ; 1053-4296
    ISSN (online) 1532-9461
    ISSN 1053-4296
    DOI 10.1016/j.semradonc.2023.03.013
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 3654: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: In Regard to Valdes et al.

    Gensheimer, Michael F / Trister, Andrew D

    International journal of radiation oncology, biology, physics

    2019  Volume 102, Issue 5, Page(s) 1593–1594

    MeSH term(s) Brachytherapy ; Machine Learning ; Salvage Therapy
    Language English
    Publishing date 2019-04-20
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 197614-x
    ISSN 1879-355X ; 0360-3016
    ISSN (online) 1879-355X
    ISSN 0360-3016
    DOI 10.1016/j.ijrobp.2018.08.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1218: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article ; Online: De-escalating elective nodal irradiation for nasopharyngeal carcinoma.

    Miller, Jacob A / Beadle, Beth M / Gensheimer, Michael F / Le, Quynh-Thu

    The Lancet. Oncology

    2022  Volume 23, Issue 4, Page(s) 441–443

    MeSH term(s) Humans ; Lymph Nodes/pathology ; Lymphatic Metastasis/radiotherapy ; Nasopharyngeal Carcinoma/pathology ; Nasopharyngeal Carcinoma/radiotherapy ; Nasopharyngeal Neoplasms/pathology ; Nasopharyngeal Neoplasms/radiotherapy ; Neoplasm Staging ; Retrospective Studies
    Language English
    Publishing date 2022-02-28
    Publishing country England
    Document type Journal Article ; Comment
    ZDB-ID 2049730-1
    ISSN 1474-5488 ; 1470-2045
    ISSN (online) 1474-5488
    ISSN 1470-2045
    DOI 10.1016/S1470-2045(22)00096-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 5696: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 460: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: Radiographic Extranodal Extension in Human Papillomavirus-Associated Oropharyngeal Carcinoma: Can it Help Tailor Treatment?

    Gensheimer, Michael F / Le, Quynh-Thu

    International journal of radiation oncology, biology, physics

    2019  Volume 104, Issue 5, Page(s) 1028–1029

    MeSH term(s) Extranodal Extension ; Humans ; Mouth Neoplasms ; Oropharyngeal Neoplasms ; Papillomaviridae ; Papillomavirus Infections
    Language English
    Publishing date 2019-07-21
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 197614-x
    ISSN 1879-355X ; 0360-3016
    ISSN (online) 1879-355X
    ISSN 0360-3016
    DOI 10.1016/j.ijrobp.2019.05.022
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1218: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer.

    Hildebrand, Rachel D / Chang, Daniel T / Ewongwoo, Agnes N / Ramchandran, Kavitha J / Gensheimer, Michael F

    JCO clinical cancer informatics

    2023  Volume 7, Page(s) e2300023

    Abstract: Purpose: For patients with cancer and their doctors, prognosis is important for choosing treatments and supportive care. Oncologists' life expectancy estimates are often inaccurate, and many patients are not aware of their general prognosis. Machine ... ...

    Abstract Purpose: For patients with cancer and their doctors, prognosis is important for choosing treatments and supportive care. Oncologists' life expectancy estimates are often inaccurate, and many patients are not aware of their general prognosis. Machine learning (ML) survival models could be useful in the clinic, but there are potential concerns involving accuracy, provider training, and patient involvement. We conducted a qualitative study to learn about patient and oncologist views on potentially using a ML model for patient care.
    Methods: Patients with metastatic cancer (n = 15) and their family members (n = 5), radiation oncologists (n = 5), and medical oncologists (n = 5) were recruited from a single academic health system. Participants were shown an anonymized report from a validated ML survival model for another patient, which included a predicted survival curve and a list of variables influencing predicted survival. Semistructured interviews were conducted using a script.
    Results: Every physician and patient who completed their interview said that they would want the option for the model to be used in their practice or care. Physicians stated that they would use an AI prognosis model for patient triage and increasing patient understanding, but had concerns about accuracy and explainability. Patients generally said that they would trust model results completely if presented by their physician but wanted to know if the model was being used in their care. Some reacted negatively to being shown a median survival prediction.
    Conclusion: Patients and physicians were supportive of use of the model in the clinic, but had various concerns, which should be addressed as predictive models are increasingly deployed in practice.
    MeSH term(s) Humans ; Prognosis ; Neoplasms/diagnosis ; Neoplasms/therapy ; Neoplasms/pathology ; Physicians ; Oncologists ; Attitude
    Language English
    Publishing date 2023-07-21
    Publishing country United States
    Document type Journal Article
    ISSN 2473-4276
    ISSN (online) 2473-4276
    DOI 10.1200/CCI.23.00023
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Article: A scalable discrete-time survival model for neural networks.

    Gensheimer, Michael F / Narasimhan, Balasubramanian

    PeerJ

    2019  Volume 7, Page(s) e6257

    Abstract: There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids ...

    Abstract There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.
    Language English
    Publishing date 2019-01-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2703241-3
    ISSN 2167-8359
    ISSN 2167-8359
    DOI 10.7717/peerj.6257
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Adaptive radiotherapy for head and neck cancer: Are we ready to put it into routine clinical practice?

    Gensheimer, Michael F / Le, Quynh-Thu

    Oral oncology

    2018  Volume 86, Page(s) 19–24

    Abstract: Patients with head and neck cancer who are treated with radiotherapy often have significant weight loss or tumor regression during treatment. Adaptive radiotherapy refers to acquiring new imaging during treatment and changing the parameters of the ... ...

    Abstract Patients with head and neck cancer who are treated with radiotherapy often have significant weight loss or tumor regression during treatment. Adaptive radiotherapy refers to acquiring new imaging during treatment and changing the parameters of the radiation plan based on the new imaging findings. There is accumulating evidence that adaptive radiotherapy can reduce toxicity and improve tumor control, though it is not yet known which patients benefit most. For patients with profound tumor regression, there is also uncertainty about how much to shrink the region receiving high radiation dose. Another form of adaptive radiotherapy uses advanced imaging such as positron emission tomography to visualize changes in tumor biology during treatment. Tumor regions that are thought to be more radioresistant can then be treated to a higher radiation dose, and vice-versa. Studies employing this strategy to boost radiation dose have shown a high rate of late toxicity, specifically the development of persistent mucosal ulcers. Therefore, this sort of adaptive radiotherapy is currently confined to the research setting.
    MeSH term(s) Clinical Trials as Topic ; Dose Fractionation, Radiation ; Dose-Response Relationship, Radiation ; Humans ; Nasopharyngeal Carcinoma/diagnostic imaging ; Nasopharyngeal Carcinoma/pathology ; Nasopharyngeal Carcinoma/radiotherapy ; Nasopharyngeal Neoplasms/diagnostic imaging ; Nasopharyngeal Neoplasms/pathology ; Nasopharyngeal Neoplasms/radiotherapy ; Positron-Emission Tomography ; Radiation Injuries/etiology ; Radiation Injuries/prevention & control ; Radiation Tolerance ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted/methods ; Radiotherapy Planning, Computer-Assisted/trends ; Radiotherapy, Image-Guided/adverse effects ; Radiotherapy, Image-Guided/methods ; Radiotherapy, Image-Guided/trends ; Radiotherapy, Intensity-Modulated/adverse effects ; Radiotherapy, Intensity-Modulated/methods ; Radiotherapy, Intensity-Modulated/trends ; Squamous Cell Carcinoma of Head and Neck/diagnostic imaging ; Squamous Cell Carcinoma of Head and Neck/pathology ; Squamous Cell Carcinoma of Head and Neck/radiotherapy ; Treatment Outcome ; Tumor Burden/radiation effects
    Language English
    Publishing date 2018-09-10
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1120465-5
    ISSN 1879-0593 ; 0964-1955 ; 1368-8375
    ISSN (online) 1879-0593
    ISSN 0964-1955 ; 1368-8375
    DOI 10.1016/j.oraloncology.2018.08.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 4869: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.A 456: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Article ; Online: Uncovering interpretable potential confounders in electronic medical records.

    Zeng, Jiaming / Gensheimer, Michael F / Rubin, Daniel L / Athey, Susan / Shachter, Ross D

    Nature communications

    2022  Volume 13, Issue 1, Page(s) 1014

    Abstract: Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured ... ...

    Abstract Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.
    MeSH term(s) Causality ; Electronic Health Records ; Humans ; Lung Neoplasms/drug therapy ; Male ; Observational Studies as Topic ; Randomized Controlled Trials as Topic ; Research Design
    Language English
    Publishing date 2022-02-23
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-28546-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top