Tanja Jutzi

National Center for Tumor Diseases

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Titus J. Brinker

German Cancer Research Center

Eva Krieghoff‐Henning

National Center for Tumor Diseases

Achim Hekler

National Center for Tumor Diseases

Jochen Utikal

German Cancer Research Center

Christof von Kalle

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

Stefan Fröhling

National Center for Tumor Diseases

Max Schmitt

National Center for Tumor Diseases

Jakob Nikolas Kather

University Hospital Carl Gustav Carus

Markus V. Heppt

Friedrich-Alexander-Universität Erlangen-Nürnberg

Heinz Kutzner

Dermatopathologie Friedrichshafen

Cooperative Institutions

Heidelberg University

German Cancer Research Center

National Center for Tumor Diseases

University Hospital Heidelberg

Karolinska Institutet

Deutschen Konsortium für Translationale Krebsforschung

DKFZ-ZMBH Alliance

Vall d'Hebron Institute of Oncology

Karolinska University Hospital

Vall d'Hebron Hospital Universitari

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Frontiers in Medicine (2020)

Tanja Jutzi Eva Krieghoff‐Henning Tim Holland‐Letz Jochen Utikal Axel Hauschild

Background: Artificial intelligence has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients’ concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients’ view of artificial intelligence in melanoma diagnostics in Germany. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients’ expectations and concerns towards artificial intelligence in general as well as their attitudes towards different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 people (154 with melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.913 – 0.967] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.846 – 0.919] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.350 – 0.462] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.917 – 0.969 to its use as assistant system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude towards the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Categorical variable

10.3389/fmed.2020.00233

Cite

Citations (131)

Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze

JDDG Journal der Deutschen Dermatologischen Gesellschaft (2020)

Heinz Kutzner Tanja Jutzi Dieter Krahl Eva Krieghoff‐Henning Markus V. Heppt

Zusammenfassung Das maligne Melanom ist diejenige Form von Hautkrebs, an der die meisten Menschen sterben. Im Frühstadium ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig. Kritiker bemängeln, dass seit der bundesweiten Einführung des Hautkrebs‐Screenings häufiger Melanome diagnostiziert werden, die Sterblichkeit am malignen Melanom jedoch nicht zurückgegangen ist. Sie führen dies vor allem auf Überdiagnosen zurück. Ein Grund ist die zum Teil komplexe Unterscheidung zwischen benignen und malignen Läsionen. Hinzu kommt, dass es auch Übergangsformen zwischen eindeutig gut‐ oder bösartigen Läsionen geben kann, und dass einige bösartige Läsionen so wenig aggressiv wachsen, dass sie nie lebensbedrohlich geworden wären. Bisher kann mangels entsprechender Biomarker nicht festgestellt werden, bei welchen Melanomen dies der Fall ist. Auch die Progressionswahrscheinlichkeit eines In‐situ‐Melanoms zu einem invasiven Tumor kann bisher nicht sicher beurteilt werden. Die Konsequenzen für überdiagnostizierte benigne Läsionen sind unnötige psychische und körperliche Belastungen für die Betroffenen und anfallende Therapiekosten. Umgekehrt können Unterdiagnosen zu gravierenden Einschränkungen der Prognose der Betroffenen und zur Notwendigkeit belastender(er) Therapien führen. Präzisere Diagnosemöglichkeiten könnten die Anzahl der korrekten Diagnosen erhöhen. Hier haben sich in Studien mit auf künstlicher Intelligenz basierenden Assistenzsystemen bereits erste Erfolge gezeigt, die allerdings noch in die klinische und pathologische Routine übertragen werden müssen.

10.1111/ddg.14233_g

Cite

Citations (0)

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

European Journal of Cancer (2021)

Sara Kuntz Eva Krieghoff‐Henning Jakob Nikolas Kather Tanja Jutzi Julia Höhn

Histology

10.1016/j.ejca.2021.07.012

Cite

Citations (127)

Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study (Preprint)

Sarah Haggenmüller Eva Krieghoff‐Henning Tanja Jutzi Nicole Trapp Lennard Kiehl

BACKGROUND Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. METHODS We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling. RESULTS We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (<i>P</i>=.02) and individuals that were familiar with the use of health or fitness apps (<i>P</i>=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

Preprint

Conjoint Analysis

mHealth

10.2196/preprints.22909

Cite

Citations (0)

Skin Cancer Classification Using Convolutional Neural Networks with Integrated Patient Data: A Systematic Review (Preprint)

Journal of Medical Internet Research (2021)

Julia Höhn Achim Hekler Eva Krieghoff‐Henning Jakob Nikolas Kather Jochen Utikal

Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers.

Contextual image classification

10.2196/20708

Cite

Citations (46)

Computerassistierte Hautkrebsdiagnose

Der Hautarzt (2020)

Titus J. Brinker Gerolf Schlager L. E. French Tanja Jutzi Harald Kittler

10.1007/s00105-020-04662-8

Cite

Citations (5)

Detergent fractionation with subsequent subtractive suppression hybridization as a tool for identifying genes coding for plasma membrane proteins

Experimental Dermatology (2009)

Andreas Lange Claudia Kistler Tanja Jutzi Alexandr V. Bazhin Claus D. Klemke

Abstract: The identification of tumor‐specific proteins located at the plasma membrane is hampered by numerous methodological pitfalls many of which are associated with the post‐translational modification of such proteins. Here, we present a new combination of detergent fractionation of cells and of subtractive suppression hybridization (SSH) to gain overexpressed genes coding for membrane‐associated or secreted proteins. Fractionation of subcellular components by digitonin allowed sequestering mRNA of the rough Endoplasmatic reticulum and thereby increasing the percentage of sequences coding for membrane‐bound proteins. Fractionated mRNAs from the cutaneous T‐cell lymphoma (CTCL) cell line HuT78 and from normal peripheral blood monocytes were used for SSH leading to the enrichment of sequences overexpressed in the tumor cells. We identified some 21 overexpressed genes, among them are GPR137B, FAM62A, NOMO1, HSP90, SLIT1, IBP2, CLIF, IRAK and ARC. mRNA expression was tested for selected genes in CTCL cell lines, skin specimens and peripheral blood samples from CTCL patients and healthy donors. Several of the detected sequences are clearly related to cancer, but have not yet been associated with CTCL. qPCR confirmed an enrichment of these mRNAs in the rough endoplasmic reticulum fraction. RT‐PCR confirmed the expression of these genes in skin specimens and peripheral blood of CTCL patients. Western blotting verified protein expression of HSP90 and IBP2 in HuT78. GPR137B could be detected by immunohistology in HuT78 and in keratinocytes of dysplastic epidermis, but also in sweat glands of healthy skin. In summary, we developed a new technique, which allows identifying overexpressed genes coding preferentially for membrane‐associated proteins.

Suppression subtractive hybridization

Subtractive color

10.1111/j.1600-0625.2008.00821.x

Cite

Citations (3)

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Laryngo-Rhino-Otologie (2022)

Tanja Jutzi Eva Krieghoff‐Henning Titus J. Brinker

The incidence of malignant melanoma is increasing worldwide. If detected early, melanoma is highly treatable, so early detection is vital.Skin cancer early detection has improved significantly in recent decades, for example by the introduction of screening in 2008 and dermoscopy. Nevertheless, in particular visual detection of early melanomas remains challenging because they show many morphological overlaps with nevi. Hence, there continues to be a high medical need to further develop methods for early skin cancer detection in order to be able to reliably diagnosemelanomas at a very early stage.Routine diagnostics for melanoma detection include visual whole body inspection, often supplemented by dermoscopy, which can significantly increase the diagnostic accuracy of experienced dermatologists. A procedure that is additionally offered in some practices and clinics is wholebody photography combined with digital dermoscopy for the early detection of malignant melanoma, especially for monitoring high-risk patients.In recent decades, numerous noninvasive adjunctive diagnostic techniques were developed for the examination of suspicious pigmented moles, that may have the potential to allow improved and, in some cases, automated evaluation of these lesions. First, confocal laser microscopy should be mentioned here, as well as electrical impedance spectroscopy, multiphoton laser tomography, multispectral analysis, Raman spectroscopy or optical coherence tomography. These diagnostic techniques usually focus on high sensitivity to avoid malignant melanoma being overlooked. However, this usually implies lower specificity, which may lead to unnecessary excision of benign lesions in screening. Also, some of the procedures are time-consuming and costly, which also limits their applicability in skin cancer screening. In the near future, the use of artificial intelligence might change skin cancer diagnostics in many ways. The most promising approach may be the analysis of routine macroscopic and dermoscopic images by artificial intelligence.For the classification of pigmented skin lesions based on macroscopic and dermoscopic images, artificial intelligence, especially in form of neural networks, has achieved comparable diagnostic accuracies to dermatologists under experimental conditions in numerous studies. In particular, it achieved high accuracies in the binary melanoma/nevus classification task, but it also performed comparably well to dermatologists in multiclass differentiation of various skin diseases. However, proof of the basic applicability and utility of such systems in clinical practice is still pending. Prerequisites that remain to be established to enable translation of such diagnostic systems into dermatological routine are means that allow users to comprehend the system's decisions as well as a uniformly high performance of the algorithms on image data from other hospitals and practices.At present, hints are accumulating that computer-aided diagnosis systems could provide their greatest benefit as assistance systems, since studies indicate that a combination of human and machine achieves the best results. Diagnostic systems based on artificial intelligence are capable of detecting morphological characteristics quickly, quantitatively, objectively and reproducibly, and could thus provide a more objective analytical basis - in addition to medical experience.Weltweit steigt die Inzidenz des malignen Melanoms an. Bei frühzeitiger Erkennung ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig.Die Hautkrebs-Früherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einführung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von frühen Melanomen eine Herausforderung, weil diese viele morphologische Überlappungen mit Nävi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfrüherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr frühen Stadium sicher diagnostizieren zu können.Die Routinediagnostik zur Hautkrebs-Früherkennung umfasst die visuelle Ganzkörperinspektion, oft ergänzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Hautärzte deutlich erhöhen lässt. Ein Verfahren, was in einigen Praxen und Kliniken zusätzlich angeboten wird, ist die kombinierte Ganzkörperfotografie mit der digitalen Dermatoskopie für die Früherkennung maligner Melanome, insbesondere für das Monitoring von Hochrisiko-Patienten.In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verdächtiger Pigmentmale entwickelt, die das Potenzial haben könnten, eine verbesserte und z. T. automatisierte Bewertung dieser Läsionen zu ermöglichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Kohärenztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivität, um zu vermeiden, ein malignes Melanom zu übersehen. Dies bedingt allerdings üblicherweise eine geringere Spezifität, was im Screening zu unnötigen Exzisionen vieler gutartiger Läsionen führen kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv,was die Anwendbarkeit im Screening ebenfalls einschränkt.In naher Zukunft wird insbesondere die Nutzung von künstlicher Intelligenz die Diagnosefindung in vielfältiger Weise verändern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch künstliche Intelligenz. Für die Klassifizierung von pigmentierten Hautläsionen anhand makroskopischer und dermatoskopischer Bilder erzielte die künstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der binären Klassifikationsaufgabe Melanom/Nävus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grundsätzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen für die Translation solcher Diagnosesysteme in die dermatologischen Routine sind Möglichkeiten für die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.Derzeit zeichnet sich ab, dass computergestützte Diagnosesysteme als Assistenzsysteme den größten Nutzen bringen könnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf künstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und könnten somit die Medizin auf eine mathematische Grundlage stellen – zusätzlich zur ärztlichen Erfahrung.

10.1055/a-1949-3639

Cite

Citations (0)

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer

BJU International (2021)

Frederik Wessels Max Schmitt Eva Krieghoff‐Henning Tanja Jutzi Thomas Stefan Worst

To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM.In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Histology

10.1111/bju.15386

Cite

Citations (50)

The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology

Nature Cancer (2022)

David Tamborero Rodrigo Dienstmann Maan Haj Rachid Jorrit Boekel Adrià López‐Fernández

There is a growing need for systems that efficiently support the work of medical teams at the precision-oncology point of care. Here, we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system developed under the umbrella of Cancer Core Europe that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data. Automating the interpretation and reporting of sequencing results decrease the need for time-consuming manual procedures that are prone to errors. The adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promotes consistent decision-making and structured data capture across the connected centers. The use of information-rich patient reports with interactive content facilitates collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.

Precision oncology

Clinical Oncology

10.1038/s43018-022-00332-x

Cite

Citations (84)