Role of nuclear bodies in apoptosis signalling

AbstractPromyelocytic leukemia nuclear bodies (PML NBs) are dynamic macromolecular multiprotein complexes that recruit and release a plethora of proteins. A considerable number of PML NB components play vital roles in apoptosis, senescence regulation and tumour suppression. The molecular basis by which PML NBs control these cellular responses is still just beginning to be understood. In addition to PML itself, numerous further tumour suppressors including transcriptional regulator p53, acetyl transferase CBP (CREB binding protein) and protein kinase HIPK2 (homeodomain interacting protein kinase 2) are recruited to PML NBs in response to genotoxic stress or oncogenic transformation and drive the senescence and apoptosis response by regulating p53 activity. Moreover, in response to death-receptor activation, PML NBs may act as nuclear depots that release apoptotic factors, such as the FLASH (FLICE-associated huge) protein, to amplify the death signal. PML NBs are also associated with other nuclear domains including Cajal bodies and nucleoli and share apoptotic regulators with these domains, implying crosstalk between NBs in apoptosis regulation. In conclusion, PML NBs appear to regulate cell death decisions through different, pathway-specific molecular mechanisms

Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study

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

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. Moreover, the impact of XAI methods on dermatologists has not yet been evaluated. Extending on two existing classifiers, we developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists alongside its differential diagnoses of melanomas and nevi. To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support. We showed that our XAI's explanations were highly aligned with clinicians' explanations and that both the clinicians' trust in the support system and their confidence in their diagnoses were significantly increased when using our XAI compared to using a conventional AI system. The clinicians' diagnostic accuracy was numerically, albeit not significantly, increased. This work demonstrates that clinicians are willing to adopt such an XAI system, motivating their future use in the clinic

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

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

Additional results derived by using different fusion approaches: Dist-opt means weighted by the distance to the individual models optimal thresholds; dist-05 means weighted by the distance to the default threshold of 0.5; avg denotes the fusion by conducting a simple average of all scores; perf means weighted based on the individual models validation performance in a way that better performing models contribute more to the fused result.

Additional results derived by using different fusion approaches: Dist-opt means weighted by the distance to the individual models optimal thresholds; dist-05 means weighted by the distance to the default threshold of 0.5; avg denotes the fusion by conducting a simple average of all scores; perf means weighted based on the individual models validation performance in a way that better performing models contribute more to the fused result.</p

ROC plots by data modality with corresponding AUROC values.

A: Results from Dresden B: Results from Erlangen C: Results from Naples. Red: MelanA-based performance taking all magnifications into account Purple: H&E-based performance Black: combined model using H&E as well as MelanA by aggregating the individual scores. (TIF)</p

S2 Dataset -

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.</div

Schematic diagram of the different models.

The red box shows the pipeline for MelanA-stained WSIs and the purple box the pipeline for H&E-stained WSIs. We tessellated MelanA-stained WSIs corresponding to different magnifications and trained individual models on each tile size. The class probabilities for each tile were predicted and aggregated into a slide score by averaging all tile scores. For the H&E-based model we proceeded in the same way.</p

Description of the population in our datasets.

For continuous features we report median, range, and number of NAs, for categorical features we report the total number of observations per group. Here the training population as well as all three test populations are described. Melanoma in situ describes the early stage of a malignant melanoma that has not yet broken through the basement membrane. However, features at the cellular level do not differ between melanoma in situ and malignant melanoma.</p

S5 Dataset -

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.</div