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1.
Ann Chir Plast Esthet ; 69(4): 326-330, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38866678

ABSTRACT

Tracheoesophageal fistulas (TOF) following esophagectomy for esophageal cancer are rare but potentially fatal. There is no consensus on treatment between stenting and surgical repair, although the latter is associated with better distant survival. In surgical repair, the interposition of a flap improves healing by providing well-vascularized tissue and reinforcing the repair zone. The flaps described are usually muscular and decaying. We present the case of a malnourished fifty-year-old man who underwent intrathoracic surgical repair of symptomatic recurrent TOF using a skin flap based on the perforators of the internal thoracic artery (IMAP). The perforator flap was completely de-epidermized and tunneled under the sternum by a proximal and limited resection of the 3rd costal cartilage and placed at the posterior aspect of the trachea, with the excess tissue rolled up on either side. At 9 months, the patient showed no recurrence and improved general condition. The de-epidermized IMAP tunneled under the sternum intrathoracically is a reliable alternative to the conventional muscle flaps described in TOF management and an attractive additional tool in the plastic surgeon's surgical arsenal.


Subject(s)
Mammary Arteries , Perforator Flap , Tracheoesophageal Fistula , Humans , Male , Mammary Arteries/surgery , Middle Aged , Tracheoesophageal Fistula/surgery , Esophagectomy/methods , Esophageal Neoplasms/surgery
2.
ESMO Open ; 9(6): 103591, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38878324

ABSTRACT

BACKGROUND: Six thoracic pathologists reviewed 259 lung neuroendocrine tumours (LNETs) from the lungNENomics project, with 171 of them having associated survival data. This cohort presents a unique opportunity to assess the strengths and limitations of current World Health Organization (WHO) classification criteria and to evaluate the utility of emerging markers. PATIENTS AND METHODS: Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm2. We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning algorithm was trained to uncover previously unnoticed morphological features with diagnostic value. RESULTS: The accuracy in distinguishing typical from ACs is hampered by interobserver variability in mitotic counting and the limitations of morphological criteria in identifying aggressive cases. Our study reveals that different Ki-67 cut-offs can categorise LNETs similarly to current WHO criteria. Counting mitoses in PHH3+ areas does not improve diagnosis, while providing a similar prognostic value to the current criteria. With the advantage of being time efficient, automated assessment of these markers leads to similar conclusions. Lastly, state-of-the-art deep learning modelling does not uncover undisclosed morphological features with diagnostic value. CONCLUSIONS: This study suggests that the mitotic criteria can be complemented by manual or automated assessment of Ki-67 or PHH3 protein expression, but these markers do not significantly improve the prognostic value of the current classification, as the AC group remains highly unspecific for aggressive cases. Therefore, we may have exhausted the potential of morphological features in classifying and prognosticating LNETs. Our study suggests that it might be time to shift the research focus towards investigating molecular markers that could contribute to a more clinically relevant morpho-molecular classification.


Subject(s)
Lung Neoplasms , Neuroendocrine Tumors , Humans , Lung Neoplasms/pathology , Lung Neoplasms/classification , Neuroendocrine Tumors/pathology , Neuroendocrine Tumors/classification , Female , Ki-67 Antigen/metabolism , Male , Biomarkers, Tumor/metabolism , Middle Aged , World Health Organization , Histones/metabolism , Aged , Prognosis , Deep Learning
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