The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review.

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

Pulmonary neuroendocrine neoplasms with well differentiated morphology and high proliferative activity: illustrated by a case series and review of the literature.

OBJECTIVES: Pulmonary neuroendocrine neoplasms (NENs) are subdivided in carcinoids and neuroendocrine carcinomas (small cell lung carcinoma and large cell neuroendocrine carcinoma (LCNEC)), based on the presence of necrosis and mitotic index (MI). However, it is unclear if tumors with well differentiated morphology but high proliferation rate should be regarded as LCNEC or as high grade carcinoids. In previous case series, a longer overall survival then expected in LCNEC has been suggested. We describe 7 of those cases analyzed for pRb expression and overall survival. MATERIAL AND METHODS: Cases with well differentiated morphology, but MI > 10/2mm2 and/or Ki-67 proliferation index >20% were selected based on pathology reports of consecutive NENs in our university medical center (Maastricht UMC+, 2007-2018) and confirmed by pathological review. Immunohistochemistry was performed to assess pRb expression. RESULTS: Seven stage IV cases were included in this study. Median overall survival was 8 months (95% confidence interval 5-11 months). Cases with well differentiated morphology and preserved pRb expression (4/7) had a median overall survival of 45 months. CONCLUSION: A subgroup of pulmonary NENs with well differentiated morphology but high proliferation rate likely exists. pRb staining might be helpful to predict prognosis, but clinical relevance remains to be studied.

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC).

OBJECTIVES: Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC. MATERIALS AND METHODS: Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients. RESULTS: Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like). CONCLUSION: LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.

Large cell neuroendocrine carcinoma with a solitary brain metastasis and low Ki-67: a unique subtype.

INTRODUCTION: Stage IV large cell neuroendocrine carcinoma (LCNEC) of the lung generally presents as disseminated and aggressive disease with a Ki-67 proliferation index (PI) 40-80%. LCNEC can be subdivided in two main subtypes: the first harboring TP53/RB1 mutations (small-cell lung carcinoma (SCLC)-like), the second with mutations in TP53 and STK11/KEAP1 (non-small-cell lung carcinoma (NSCLC)-like). Here we evaluated 11 LCNEC patients with only a solitary brain metastasis and evaluate phenotype, genotype and follow-up. METHODS: Eleven LCNEC patients with solitary brain metastases were analyzed. Clinical characteristics and survival data were retrieved from medical records. Pathological analysis included histomorphological analysis, immunohistochemistry (pRB and Ki-67 PI) and next-generation sequencing (TP53, RB1, STK11, KEAP1 and MEN1). RESULTS: All patients had N0 or N1 disease. Median overall survival (OS) was 12 months (95% confidence interval (CI) 5.5-18.5 months). Mean Ki-67 PI was 59% (range 15-100%). In 6/11 LCNEC Ki-67 PI was ≤40%. OS was longer for Ki-67 ≤40% compared to >40% (17 months (95% CI 11-23 months) vs 5 months (95% CI 0.7-9 months), P = 0.007). Two patients were still alive at follow-up after 86 and 103 months, both had Ki-67 ≤40%. 8/11 patients could be subclassified, and both SCLC-like (n = 6) and NSCLC-like (n = 2) subtypes were present. No MEN1 mutation was found. CONCLUSION: Stage IV LCNEC with a solitary brain metastasis and N0/N1 disease show in the majority of cases Ki-67 PI ≤40% and prolonged survival, distinguishing them from general LCNEC. This unique subgroup can be both of the SCLC-like and NSCLC-like subtype.

Rapid Identification of Ischemic Injury in Renal Tissue by Mass-Spectrometry Imaging.

The increasing analytical speed of mass-spectrometry imaging (MSI) has led to growing interest in the medical field. Acute kidney injury is a severe disease with high morbidity and mortality. No reliable cut-offs are known to estimate the severity of acute kidney injury. Thus, there is a need for new tools to rapidly and accurately assess acute ischemia, which is of clinical importance in intensive care and in kidney transplantation. We investigated the value of MSI to assess acute ischemic kidney tissue in a porcine model. A perfusion model was developed where paired kidneys received warm (severe) or cold (minor) ischemia ( n = 8 per group). First, ischemic tissue damage was systematically assessed by two blinded pathologists. Second, MALDI-MSI of kidney tissues was performed to study the spatial distributions and compositions of lipids in the tissues. Histopathological examination revealed no significant difference between kidneys, whereas MALDI-MSI was capable of a detailed discrimination of severe and mild ischemia by differential expression of characteristic lipid-degradation products throughout the tissue within 2 h. In particular, lysolipids, including lysocardiolipins, lysophosphatidylcholines, and lysophosphatidylinositol, were dramatically elevated after severe ischemia. This study demonstrates the significant potential of MSI to differentiate and identify molecular patterns of early ischemic injury in a clinically acceptable time frame. The observed changes highlight the underlying biochemical processes of acute ischemic kidney injury and provide a molecular classification tool that can be deployed in assessment of acute ischemic kidney injury.