ABSTRACT
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.
Subject(s)
Carcinoma, Basal Cell , Skin Neoplasms , Humans , Skin Neoplasms/diagnostic imaging , Skin Neoplasms/pathology , Artificial Intelligence , Sensitivity and Specificity , Dermoscopy/methods , Carcinoma, Basal Cell/diagnostic imaging , Carcinoma, Basal Cell/pathology , Tomography, Optical Coherence , Microscopy, Confocal/methodsABSTRACT
BACKGROUND: Breast cancer is the most frequent form of cancer among women worldwide. Reconstructive surgery may improve the quality of life (QoL), after mastectomy. Various techniques are used to reconstruct the female breast; however, few is known about its specific post-surgery influence represented in patient-reported outcomes. OBJECTIVE: This systematic review assesses the difference in patient-reported QoL between prosthetic reconstruction alone, and prosthetic reconstruction with additional autologous fat transfer (AFT). DATA SOURCES: A literature search was performed in PubMed, Embase, Cochrane and CINAHL online databases from inception to February 11th, 2020. STUDY SELECTION: Inclusion and exclusion criteria were used to assess the eligibility of the retrieved articles. The only eligible studies were cohort studies. DATA COLLECTION AND ANALYSIS: Relevant data for the research question was extracted from the articles and systematically documented. Results not contributing to answering the objective were intentionally left out. No meta-analysis was realized. RESULTS: This systematic review resulted in the inclusion of only six relevant studies, all cohort studies, consisting of 1437 unique patients. These studies evaluated the quality of life of patients by means of the validated BREAST-Q questionnaire. Outcomes varied for which reason no definite answer could be provided to whether additional AFT results in a higher QoL. CONCLUSIONS: It is unclear whether additional AFT after prosthetic surgery leads to a higher QoL when compared to sole prosthetic reconstruction or not. Additional studies, assessing the QoL of patients who received additional AFT, are required to draw solid conclusions. LEVEL OF EVIDENCE: Level III; systematic literature review of cohort studies.
