Objective
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.
Patients and Methods
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.
Results
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.
Conclusion
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.
Overdiagnosis of melanoma – causes, consequences and solutions Kutzner, Heinz; Jutzi, Tanja B.; Krahl, Dieter ...
Journal der Deutschen Dermatologischen Gesellschaft,
November 2020, 2020-Nov, 2020-11-00, 20201101, Letnik:
18, Številka:
11
Journal Article
Recenzirano
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.
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. ...The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.
Background:
Artificial intelligence (AI) 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, with a particular focus on patients with a history of melanoma.
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 toward artificial intelligence in general as well as their attitudes toward 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 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% 95% CI = 0.91–0.97 of respondents supported the use of artificial intelligence in medical approaches. 88% 95% CI = 0.85–0.92 would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% 95% CI = 0.35–0.46 of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% 95% CI = 0.92–0.97 to its use as assistance 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 toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
Over the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the ...enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance.
PubMed was screened for peer-reviewed articles published in English between January 2015 and June 2021 by two independent researchers. Search terms related to deep learning, digital pathology, omics, and multimodal fusion were combined.
We identified a total of 11 studies meeting the inclusion criteria, namely studies that used convolutional neural networks for haematoxylin and eosin image analysis of patients with cancer in combination with integrated omics data. Publications were categorised according to their endpoints: 7 studies focused on survival analysis and 4 studies on prediction of cancer subtypes, malignancy or microsatellite instability with spatial analysis.
Image-based classifiers already show high performances in prognostic and predictive cancer diagnostics. The integration of omics data led to improved performance in all studies described here. However, these are very early studies that still require external validation to demonstrate their generalisability and robustness. Further and more comprehensive studies with larger sample sizes are needed to evaluate performance and determine clinical benefits.
•Fusion of image and genomic data with deep learning improves performance.•Several studies showed a benefit for cancer research.•The studies are at a very early stage and require external validation.•Further studies are needed to demonstrate clinical benefits.
Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a ...digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours.
A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status.
The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% ± 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% ± 3.5%) AUROC or less.
Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts.
•Lymph node analysis conveys relevant prognostic information.•Surgical removal of lymph nodes can be associated with considerable morbidity.•Thus, biomarkers that allow prediction of lymph node status are needed.•We trained a CNN on H&E slides of node positive and -negative primary melanomas.•This classifier could predict sentinel node status to some extent.
In machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also rely on a range of information from ...different examinations for diagnosis. In this study, we used BRAF mutation status prediction in melanoma as a model system to analyse the contribution of different data types in a combined classifier because BRAF status can be determined accurately by sequencing as the current gold standard, thus nearly eliminating label noise.
We trained a deep learning-based classifier by combining individually trained random forests of image, clinical and methylation data to predict BRAF-V600 mutation status in primary and metastatic melanomas of The Cancer Genome Atlas cohort.
With our multimodal approach, we achieved an area under the receiver operating characteristic curve of 0.80, whereas the individual classifiers yielded areas under the receiver operating characteristic curve of 0.63 (histopathologic image data), 0.66 (clinical data) and 0.66 (methylation data) on an independent data set.
Our combined approach can predict BRAF status to some extent by identifying BRAF-V600 specific patterns at the histologic, clinical and epigenetic levels. The multimodal classifiers have improved generalisability in predicting BRAF mutation status.
•A combined DL classifier predicts BRAF-V600 mutations in malignant melanoma.•Combining data modalities improves performance and generalisation capability.•Multimodal classifiers have the potential to be used as predictive biomarkers.
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.
For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the ...potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation SD = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval CI: 62.9–68.1%), 86.2% (95%-CI: 81.8–90.5%), 44.9% (95%-CI: 40.2–49.6%), and 0.70 (95%-CI: 0.69–0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70–8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60–5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92–4.94, p = 0.08) on external validation. The results demonstrate that the CNN’s image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.
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