Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas.
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ABSTRACT:
SUBMITTER: Ali SR
PROVIDER: S-EPMC10416688 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
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Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas.
Ali Stephen R SR Dobbs Thomas D TD Jovic Matthew M Strafford Huw H Fonferko-Shadrach Beata B Lacey Arron S AS Williams Namor N Pickrell William Owen WO Hutchings Hayley A HA Whitaker Iain S IS
The British journal of surgery 20230801 9
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Project description:BackgroundAchieving adequate surgical margins and preventing recurrence are important in the treatment of basal cell carcinoma (BCC).ObjectivesThe objectives of this study were to evaluate the adequacy of surgical margins and the re-excision rates in patients with primary BCC who underwent standard surgical treatment using our proposed algorithm and to define the risk factors in patients with recurrent BCC.MethodsThe medical records of patients who were histopathologically diagnosed with BCC were reviewed. An algorithm created based on previous literature was used to determine the distribution of optimal surgical margins adequacy and re-excision rates.ResultsStatistically significant differences were observed between the cases with and without recurrence in age at diagnosis (p=0.004), tumor size (p=0.023), tumor location in the H zone of the face (p=0.005), and aggressive histopathological subtype (p=0.000). When the tumors were evaluated for adequacy of deep and lateral surgical margins and re-excision rates, higher rates of adequate excision (457 cases, 68.0%) and re-excision (43 cases, 33.9%) were noted for tumors in the H or M zone.Study limitationsInadequate follow-up of newly diagnosed patients in terms of recurrence and metastasis and the retrospective application of our proposed algorithm are the limitations of the present study.ConclusionsOur results showed that if BCC was detected at an early age and at an early stage, recurrence was lower. The H and M zones were the regions with the highest rates of optimal surgical outcomes.
| S-EPMC10334344 | biostudies-literature
Project description:Basal cell carcinomas (BCCs) are very common epithelial cancers that depend on the Hedgehog pathway for tumor growth. Traditional therapies such as surgical excision are effective for most patients with sporadic BCC; however, better treatment options are needed for cosmetically sensitive or advanced and metastatic BCC. The first approved Hedgehog antagonist targeting the membrane receptor Smoothened, vismodegib, shows remarkable effectiveness on both syndromic and nonsyndromic BCCs. However, drug-resistant tumors frequently develop, illustrating the need for the development of next-generation Hedgehog antagonists targeting pathway components downstream from Smoothened. In this article, we will summarize available BCC treatment options and discuss the development of next-generation antagonists.
| S-EPMC4066644 | biostudies-literature
Project description:IntroductionRoutinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data.MethodsWe used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care.ResultsWe identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85.ConclusionUsing an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.
| S-EPMC9683031 | biostudies-literature
Project description:The majority of non-melanoma skin cancer (NMSC) is cutaneous basal cell carcinoma (BCC) or squamous cell carcinoma (SCC), which are also called keratinocyte carcinomas, as both of them originate from keratinocytes. The incidence of keratinocyte carcinomas is over 5 million per year in the US, three-fold higher than the total incidence of all other types of cancer combined. While there are several reports on gene expression profiling of BCC and SCC, there are significant variations in the reported gene expression changes in different studies. One reason is that tumor-adjacent normal skin specimens were not included in many studies as matched controls. Furthermore, while numerous studies of skin stem cells in mouse models have been reported, their relevance to human skin cancer remains unknown. In this report, we analyzed gene expression profiles of paired specimens of keratinocyte carcinomas with their matched normal skin tissues as the control. Among several novel findings, we discovered a significant number of zinc finger encoding genes up-regulated in human BCC. In BCC, a novel link was found between hedgehog signaling, Wnt signaling, and the cilium. While the SCC cancer-stem-cell gene signature is shared between human and mouse SCCs, the hair follicle stem-cell signature of mice was not highly represented in human SCC. Differential gene expression (DEG) in human BCC shares gene signature with both bulge and epidermal stem cells. We have also determined that human BCCs and SCCs have distinct gene expression patterns, and some of them are not fully reflected in current mouse models.
| S-EPMC8099692 | biostudies-literature
Project description:Autonomous steroid secretion is a common feature of adrenocortical carcinomas (ACC), although not always clinically evident owing to inefficient steroidogenesis with increased release of steroid precursors. Our study aim was to analyze the expression profile of four key proteins involved in the steroidogenesis cascade, in different adrenocortical tumors. Expression of proteins involved in steroidogenesis, namely steroidogenic acute regulatory protein (StAR), 11β-hydroxylase (CYP11B1), aldosterone synthase (CYP11B2) and 17α-hydroxylase (CYP17A1), were analyzed by immunohistochemistry in ACC (n = 14), adenomas presenting with Cushing's syndrome (ACAc) (n = 11) and clinically non-functioning adenomas (ACAn) (n = 15). A percentage of the stained area for each protein was analyzed using ImageJ software for computerized morphometric quantification. CYP11B1, StAR and CYP17A1 expression were significantly lower in ACC when compared to ACAc. In addition, ACC presented co-staining cells for CYP11B1 and CYP11B2. CYP11B1 was the steroidogenic enzyme with the most discriminative power to distinguish ACC from ACAc, with a sensitivity of 100%, specificity of 92%, and an expression higher than 4.44%, indicating the presence of a cortisol secreting adenoma. ACC depicts an incomplete pattern of steroidogenic protein expression, with decreased CYP11B1 and CYP17A1, which could explain the predominant secretion of steroid precursors.
| S-EPMC7460193 | biostudies-literature
Project description:IntroductionGenerating physician letters is a time-consuming task in daily clinical practice.MethodsThis study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.ResultsOur findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters. The QLoRA algorithm provides an efficient method for local intra-institutional fine-tuning of LLMs with limited computational resources (i.e., a single 48 GB GPU workstation within the hospital). The fine-tuned LLM successfully learns radiation oncology-specific information and generates physician letters in an institution-specific style. ROUGE scores of the generated summary reports highlight the superiority of the 8B LLaMA-3 model over the 13B LLaMA-2 model. Further multidimensional physician evaluations of 10 cases reveal that, although the fine-tuned LLaMA-3 model has limited capacity to generate content beyond the provided input data, it successfully generates salutations, diagnoses and treatment histories, recommendations for further treatment, and planned schedules. Overall, clinical benefit was rated highly by the clinical experts (average score of 3.4 on a 4-point scale).DiscussionWith careful physician review and correction, automated LLM-based physician letter generation has significant practical value.
| S-EPMC11772293 | biostudies-literature
Project description:Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p [Formula: see text]), nonparametric ANOVAs showed significant group differences (p[Formula: see text]). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p[Formula: see text]) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9-75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
| S-EPMC8155086 | biostudies-literature
Project description:IntroductionPrecision radiotherapy relies on accurate segmentation of tumor targets and organs at risk (OARs). Clinicians manually review automatically delineated structures on a case-by-case basis, a time-consuming process dependent on reviewer experience and alertness. This study proposes a general process for automated threshold generation for structural evaluation indicators and patient-specific quality assurance (QA) for automated segmentation of nasopharyngeal carcinoma (NPC).MethodsThe patient-specific QA process for automated segmentation involves determining the confidence limit and error structure highlight stage. Three expert physicians segmented 17 OARs using computed tomography images of NPC and compared them using the Dice similarity coefficient, the maximum Hausdorff distance, and the mean distance to agreement. For each OAR, the 95% confidence interval was calculated as the confidence limit for each indicator. If two or more evaluation indicators (N2) or one or more evaluation indicators (N1) exceeded the confidence limits, the structure segmentation result was considered abnormal. The quantitative performances of these two methods were compared with those obtained by artificially introducing small/medium and serious errors.ResultsThe sensitivity, specificity, balanced accuracy, and F-score values for N2 were 0.944 ± 0.052, 0.827 ± 0.149, 0.886 ± 0.076, and 0.936 ± 0.045, respectively, whereas those for N1 were 0.955 ± 0.045, 0.788 ± 0.189, 0.878 ± 0.096, and 0.948 ± 0.035, respectively. N2 and N1 had small/medium error detection rates of 97.67 ± 0.04% and 98.67 ± 0.04%, respectively, with a serious error detection rate of 100%.ConclusionThe proposed automated patient-specific QA process effectively detected segmentation abnormalities, particularly serious errors. These are crucial for enhancing review efficiency and automated segmentation, and for improving physician confidence in automated segmentation.
| S-EPMC11792024 | biostudies-literature
Project description:Basal-like breast cancers, an aggressive breast cancer subtype that has poor treatment options, are thought to arise from luminal mammary epithelial cells that undergo basal plasticity through poorly understood mechanisms. Using genetic mouse models and ex vivo primary organoid cultures, we show that conditional co-deletion of the LATS1 and LATS2 kinases, key effectors of Hippo pathway signaling, in mature mammary luminal epithelial cells promotes the development of Krt14 and Sox9-expressing basal-like carcinomas that metastasize over time. Genetic co-deletion experiments revealed that phenotypes resulting from the loss of LATS1/2 activity are dependent on the transcriptional regulators YAP/TAZ. Gene expression analyses of LATS1/2-deleted mammary epithelial cells notably revealed a transcriptional program that associates with human basal-like breast cancers. Our study demonstrates in vivo roles for the LATS1/2 kinases in mammary epithelial homeostasis and luminal-basal fate control and implicates signaling networks induced upon the loss of LATS1/2 activity in the development of basal-like breast cancer.
| S-EPMC9705439 | biostudies-literature
Project description:BackgroundBasal cell carcinoma (BCC) represents by far the most common non-melanoma skin cancer (NMSC) in the world with an increasing incidence of 3% to 10% per year, especially in patients under the age of 40. While variants in the sonic Hedgehog and cell cycle regulation pathways account for the majority of BCC cases in adults, the molecular etiology of BCC in young patients is unelucidated yet. This study aims to investigate the molecular profile of BCC in the young population.Methods28 tumors belonging to 25 Lebanese patients under the age of 40, presenting different stages of BCC and diagnosed at Hôtel Dieu de France-Saint Joseph University Medical Center were included in this study. A selected panel of 150 genes involved in cancer was analyzed by Next Generation Sequencing (NGS) in the 28 included tumors.ResultsGenetic variants detected in more than 5% of the reads, with a sequencing depth ≥ 50x, were selected. Two hundred and two genetic variants in 48 different genes were detected, with an overall average sequencing depth of 1069x. Among the 28 studied tumors, 18 (64.3%) show variations in the PTCH1 gene, 6 (21.4%) in TP53 and 3 (10.7%) in SMO.ConclusionsThis is the first study reporting NGS-based analysis of BCC in a cohort of young patients. Our results highlight the involvement of the hedgehog and cell cycle regulation pathways in the genesis of BCC in the general population. The inclusion of a larger cohort of young patients is needed to confirm our findings.
| S-EPMC8293576 | biostudies-literature