ABSTRACT
Langerhans cell histiocytosis (LCH) is a disease whose physiopathology remains unclear, involving both inflammatory processes and clonal proliferation. It is observable at any given age, although about ten times more frequent in children than adults. Hepatic involvement is not rare, mostly part of a systemic disease, and linked to a poor prognosis. We report here a case of LCH with solitary hepatic involvement in a 74 year-old patient. This case demonstrated molecular anomaly of the MAPK pathway, BRAF N486_P490del. Through this observation, we precise the epidemiological and histological aspects and diagnostic criteria of this rare disease.
Subject(s)
Histiocytosis, Langerhans-Cell , Aged , Humans , Histiocytosis, Langerhans-Cell/diagnosis , Liver/pathology , Rare DiseasesABSTRACT
Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.
ABSTRACT
BACKGROUND: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
ABSTRACT
Fumarate hydratase deficient renal cell carcinoma (FH-RCC) is a rare malignant neoplasia caused by constitutive or somatic mutations in the FH gene whose diagnosis is primordial, requiring genetic counselling. Because of histological heterogeneity, such tumors have been in the past misclassified as "type 2 papillary carcinoma", "tubulo-cystic renal cell carcinoma" or "high grade papillary carcinoma". We report here a case of FH deficient renal cell carcinoma (FH-RCC) in a 69years old patient. Through this observation, we precise the epidemiological and histological aspects and diagnosis criteria of this rare tumor.
Subject(s)
Carcinoma, Papillary , Carcinoma, Renal Cell , Kidney Neoplasms , Leiomyomatosis , Skin Neoplasms , Uterine Neoplasms , Female , Humans , Carcinoma, Renal Cell/diagnosis , Fumarate Hydratase/genetics , Immunohistochemistry , Kidney Neoplasms/pathology , Leiomyomatosis/diagnosis , Skin Neoplasms/genetics , AgedABSTRACT
Testis tumors are uncommon in oncology, and testicular metastasis from distant solid tumors are even rarer. We present two cases encountered in our department of pathology in CHU de Rennes, France. Moreover, we collected all reported cases in the Medline/PubMed databases of non-hematopoietic secondary testis tumors in adults, excluding autopsy studies, to propose an integrative study on this topic. In total, we report 98 cases of secondary testis lesions to prostate (n=38, 38.77 %), colorectal (n=19, 19.39%), gastric (n=12, 12.24%), kidney (n=7, 7.14%), lung (n=6, 6.12%) and other primary cancers. The median age at diagnosis was 66.5 years. We identified significantly more prostate adenocarcinoma (P<0.0001) when the primary tumor was known and significantly more colorectal adenocarcinoma (P=0.035) and pancreatic adenocarcinoma (P=0.002) when the primary tumor was unknown. The age at diagnosis was older when the primary tumor was known (P=0.007). We present the challenges for the diagnosis and propose some elements for diagnosis orientation. Finally, we discuss the possible ways of metastatic dissemination from primary site to testis, as illustrated by the two cases we present.
Subject(s)
Adenocarcinoma , Colorectal Neoplasms , Pancreatic Neoplasms , Testicular Neoplasms , Male , Adult , Humans , Aged , Testis/pathology , Adenocarcinoma/pathology , Pancreatic Neoplasms/pathology , Testicular Neoplasms/diagnosis , Testicular Neoplasms/pathology , Colorectal Neoplasms/diagnosis , Colorectal Neoplasms/pathologyABSTRACT
Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.
