Introduction: This is the first study of which we are aware to describe the analytical validation (AV) of clinical grade artificial intelligence (AI) algorithms for a commercially available prostate cancer test performed on hematoxylin and eosin stained specimens that is not dependent on a priori established molecules or a priori semantically meaningful morphology. 

Analytical Validation of a Clinical Grade Prognostic and Classification Artificial Intelligence Laboratory Test for Men with Prostate Cancer

Analytische Validierung eines digitalen tumormorphometrischen Prognosetests

Die histopathologischen Charakteristika eines invasiven Mammakarzinoms geben Auskunft über sein biologisches Rezidivpotenzial. Allerdings ist die Beurteilung subjektiv und unterliegt einer hohen Interobserver-Variabilität. Ein neuartiger digitaler Prognosetest auf der Basis künstlicher Intelligenz sagt das Rezidivrisiko anhand repräsentativer Gewebeschnitte voraus. Ein US-Forscherteam überprüfte nun die analytische Leistungsfähigkeit des Tests. 

Background: Breast cancer (BC) is the most frequently diagnosed cancer worldwide and the second leading cause of cancer-related deaths. To advance predictive recurrence models we developed an AI-image analysis platform that utilizes whole slide images (WSI) to phenotype invasive BC (IBC) at the tissue-cell architectural level. The objective was to produce an AI-IBC phenotype that includes a novel methodology to grade BC along with additional features that reflect biological pathways. We sought to combine these extracted and complex image feature sets with standard clinical pathology data to develop readily accessible models that predict early-stage BC recurrence. 

Development and Validation of a Digital-Artificial Intelligent (AI) enabled Assay to predict early-stage breast cancer recurrence

Psycho-oncology is evolving in line with oncology progress and increasing complexity, but also with change in cancer care organization. Alongside the more traditional psycho-oncological interventions, such as the support or verbal psychotherapy of various inspirations (psychodynamic, integrative, systemic, existential) and body-mediated approaches, that allow the patient to be accompanied throughout his/her cancer care trajectory, psycho-oncology is now benefiting from the development of more structured interventions, often brief and targeted at a specific situation or symptom. This article reviews three of these new psychotherapeutic approaches, which are largely developed in the Anglo-Saxon world: the management of fear of recurrence by CBT third wave strategies, ACT therapy or, more recently, EMDR to answer to psychotraumatic situations. We describe here the principles, the main indications in oncology patients and the expected clinical benefits. It also presents tools such as Questionnaire Prompt Lists for optimizing the communication between patients and health professionals, which constitute a psychotherapeutic intervention by itself. 

[Psycho-oncological interventions: What type of psychotherapeutic innovations?]

Background : PreciseDx Breast is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. Materials and Methods : Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled Morphology Feature Array, microanatomic features are generated. Morphometric attributes in combination with patient age, tumor size, stage and lymph node status predicts disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PreciseDx Breast test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. Results : Analytical validation of features derived from whole slide images demonstrated a high-degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability was less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. Conclusion : In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients. MicroAbstract: PreciseDx Breast (PDxBr) is a digital test, which generates an artificial-intelligent tumor grade and phenotype utilizing morphometric features derived from H&E images of invasive breast cancer to predict outcome. Analytical validation of the image analysis platform including robust accuracy of cell type identification and tissue architecture composition, combined with test reproducibility and reliability, is a critical requirement for approval and clinical adoption. 

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer.

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Development and Validation of a Digital-Artificial Intelligent (AI) enabled Assay to predict early-stage breast cancer recurrence

[Psycho-oncological interventions: What type of psychotherapeutic innovations?]

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer.