Surgery for lower rectal cancer poses a significant challenge because avoiding a permanent colostomy requires technically complex low anastomosis. This article reviews the different anastomotic techniques following total mesorectal excision, with particular emphasis on the Turnbull-Cutait (T-C) technique and transanal transection with single-stapled anastomosis (TTSS). The T-C technique enables a two-stage coloanal anastomosis without the need for a protective ileostomy, thereby reducing stoma-related morbidity. Meanwhile, TTSS improves precision in distal resection and reduces the incidence of anastomotic leakage. Appropriate patient selection is key to determining the most suitable surgical strategy. Despite technical advances, the incidence of low anterior resection syndrome remains high in this patient group. Surgical decisions should be individualized, taking into account each patient’ profile and the anticipated oncological and functional outcomes.
{"title":"Anastomosis en el cáncer de recto inferior: técnicas, indicaciones y resultados funcionales","authors":"Ana Gálvez , Caterina Foppa , Antonino Spinelli , Sebastiano Biondo","doi":"10.1016/j.ciresp.2025.800129","DOIUrl":"10.1016/j.ciresp.2025.800129","url":null,"abstract":"<div><div>Surgery for lower rectal cancer poses a significant challenge because avoiding a permanent colostomy requires technically complex low anastomosis. This article reviews the different anastomotic techniques following total mesorectal excision, with particular emphasis on the Turnbull-Cutait (T-C) technique and transanal transection with single-stapled anastomosis (TTSS). The T-C technique enables a two-stage coloanal anastomosis without the need for a protective ileostomy, thereby reducing stoma-related morbidity. Meanwhile, TTSS improves precision in distal resection and reduces the incidence of anastomotic leakage. Appropriate patient selection is key to determining the most suitable surgical strategy. Despite technical advances, the incidence of low anterior resection syndrome remains high in this patient group. Surgical decisions should be individualized, taking into account each patient’ profile and the anticipated oncological and functional outcomes.</div></div>","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 7","pages":"Article 800129"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.ciresp.2025.800124
José Fernando Trebolle , Jorge Solano Murillo , Jesús Lobo Cobo , Carmen Pellicer Lostao , Mónica Valero Sabater , Gabriel Tirado Anglés , Irene Cantarero Carmona , María José Luesma Bartolomé
Objective
To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.
Methods
Two Excel® tables were generated from a FileMaker file. Python was used through a Notebook format in Google™ Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.
Results
The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.
Conclusions
The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.
{"title":"Desarrollo y validación de algoritmo predictivo de la longitud total del intestino delgado con técnicas de inteligencia artificial para su aplicación en cirugía bariátrica","authors":"José Fernando Trebolle , Jorge Solano Murillo , Jesús Lobo Cobo , Carmen Pellicer Lostao , Mónica Valero Sabater , Gabriel Tirado Anglés , Irene Cantarero Carmona , María José Luesma Bartolomé","doi":"10.1016/j.ciresp.2025.800124","DOIUrl":"10.1016/j.ciresp.2025.800124","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.</div></div><div><h3>Methods</h3><div>Two Excel® tables were generated from a FileMaker file. Python was used through a Notebook format in Google™ Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.</div></div><div><h3>Results</h3><div>The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.</div></div><div><h3>Conclusions</h3><div>The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.</div></div>","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 7","pages":"Article 800124"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.ciresp.2025.800128
Antonio Ríos , en representación del Grupo de Investigación del Proyecto Nacional Español sobre el Carcinoma Papilar Familiar avalado por la Sección de Cirugía Endocrina de la Asociación Española de Cirujanos (CPFT-AEC Ríos)
{"title":"Carcinoma papilar familiar de tiroides. La importancia de la historia clínica","authors":"Antonio Ríos , en representación del Grupo de Investigación del Proyecto Nacional Español sobre el Carcinoma Papilar Familiar avalado por la Sección de Cirugía Endocrina de la Asociación Española de Cirujanos (CPFT-AEC Ríos)","doi":"10.1016/j.ciresp.2025.800128","DOIUrl":"10.1016/j.ciresp.2025.800128","url":null,"abstract":"","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 7","pages":"Article 800128"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.ciresp.2025.800105
Marta Ruiz de la Cuesta , Celia Villodre , Mario Serradilla , Emilio Ruiz de la Cuesta , Cándido Alcazar , José M. Ramia , miembros proyecto SPANDISPAN
{"title":"Factores relacionados con hemorragia tras pancreatectomía izquierda (proyecto SPANDISPAN)","authors":"Marta Ruiz de la Cuesta , Celia Villodre , Mario Serradilla , Emilio Ruiz de la Cuesta , Cándido Alcazar , José M. Ramia , miembros proyecto SPANDISPAN","doi":"10.1016/j.ciresp.2025.800105","DOIUrl":"10.1016/j.ciresp.2025.800105","url":null,"abstract":"","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 7","pages":"Article 800105"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/S0009-739X(25)01560-X
{"title":"Junta Directiva, Comité Científico, Comité de Congresos, Comité de Congreso Local, Comité de Docencia y Formación Continuada, Comité de Relaciones Institucionales y Asuntos Profesionales","authors":"","doi":"10.1016/S0009-739X(25)01560-X","DOIUrl":"10.1016/S0009-739X(25)01560-X","url":null,"abstract":"","PeriodicalId":50690,"journal":{"name":"Cirugia Espanola","volume":"103 ","pages":"Pages I-II"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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