Copy number variation in archival melanoma biopsies versus benign melanocytic lesions

Mahas, Ahmed; Potluri, Keerti; Kent, M.; Naik, Sameep; Markey, Michael P.

doi:10.3233/cbm-160600

Cited by 4 publications

(4 citation statements)

References 230 publications

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“…Some studies indicate that, in addition to classical histology, molecular biological markers, for example the detection of copy number variations in the genome of tumor cells, may also contribute to the differentiation of melanomas and nevi [59,60]. Within limits, data on the clinical course may be used to retrospectively assess the malignity status of a lesion.…”

Section: Solutionsmentioning

confidence: 99%

Overdiagnosis of melanoma – causes, consequences and solutions

Kutzner

Jutzi

Krahl³

et al. 2020

J Deutsche Derma Gesell

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Summary Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is essential. However, the skin cancer screening program in Germany has been criticized because although melanomas have been diagnosed more frequently since introduction of the program, the mortality from malignant melanoma has not decreased. This indicates that the observed increase in melanoma diagnoses be due to overdiagnosis, i.e. to the detection of lesions that would never have created serious health problems for the patients. One of the reasons is the challenging distinction between some benign and malignant lesions. In addition, there may be lesions that are biologically equivocal, and other lesions that are classified as malignant according to current criteria, but that grow so slowly that they would never have posed a threat to patient’s life. So far, these “indolent” melanomas cannot be identified reliably due to a lack of biomarkers. Moreover, the likelihood that an in‐situ melanoma will progress to an invasive tumor still cannot be determined with any certainty. When benign lesions are diagnosed as melanoma, the consequences are unnecessary psychological and physical stress for the affected patients and incurred therapy costs. Vice versa, underdiagnoses in the sense of overlooked melanomas can adversely affect patients’ prognoses and may necessitate more intense therapies. Novel diagnostic options could reduce the number of over‐ and underdiagnoses and contribute to more objective diagnoses in borderline cases. One strategy that has yielded promising results in pilot studies is the use of artificial intelligence‐based diagnostic tools. However, these applications still await translation into clinical and pathological routine.

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Section: Solutionsmentioning

confidence: 99%

Overdiagnosis of melanoma – causes, consequences and solutions

Kutzner

Jutzi

Krahl³

et al. 2020

J Deutsche Derma Gesell

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“…Studien deuten darauf hin, dass neben der klassischen Histologie auch molekularbiologische Marker wie der Nachweis von Kopienzahlveränderungen im Genom der Tumorzellen zur Unterscheidung zwischen Melanomen und Nävi beitragen könnten [59,60]. Klinische Verlaufsdaten können in Grenzen dazu herangezogen werden, die Dignität einer Läsion im Nachhinein zu beurteilen.…”

Section: Lösungsansätzeunclassified

Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze

Kutzner

Jutzi

Krahl³

et al. 2020

J Deutsche Derma Gesell

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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.

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“…Phenotypic variation may also be due to CNVs [ 6 , 14 ]. The data obtained from CNVs can also be used to classify tumors into malignant and benign [ 15 , 16 ]. A number of research articles agree that somatic CNVs are mostly associated with the progression of various cancers [ 17 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

Attique

Shah

Jabeen³

et al. 2022

Computational Intelligence and Neuroscience

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DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

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Copy number variation in archival melanoma biopsies versus benign melanocytic lesions

Cited by 4 publications

References 230 publications

Overdiagnosis of melanoma – causes, consequences and solutions

Overdiagnosis of melanoma – causes, consequences and solutions

Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

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