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AIM: This study is aimed to explore the safety and feasibility of indocyanine green (ICG) fluorescence imaging guidance in laparoscopic para-aortic lymph node (PALN) dissection for left-sided colorectal cancer (CRC) patients with clinically suspected PALN metastasis. METHOD: A total of 151 patients who underwent primary tumor resection and laparoscopic PALN dissection for left-sided CRC were included, with 20 patients in the ICG group and 131 patients in the non-ICG group. The surgical outcomes, postoperative complications, and pathological results, such as the number of harvested and metastatic lymph nodes were compared between groups after propensity score matching. RESULTS: Following propensity score matching, the ICG group had 20 patients, and the non-ICG group had 53 patients, and the two groups were similar in baseline characteristics. No significant differences were observed in overall intraoperative and postoperative complications between groups, except for chylous leakage, where the ICG group had a longer time to a normal diet. The number of harvested pericolic/perirectal and intermediate lymph nodes were comparable between the two groups, while the ICG group had a significantly higher number of total harvested lymph nodes (39 [14-78] vs. 29 [11-70], P = 0.001), inferior mesenteric artery lymph nodes (IMALN, 6 [0-17] vs. 3 [0-11], P = 0.006), and PALNs (9 [3-29] vs. 5 [1-37], P = 0.001). CONCLUSION: ICG fluorescence imaging could increase the retrieval of IMALN, PALN, and total lymph nodes, and potentially improve the completeness of laparoscopic PALN dissection in patients with left-sided CRC.
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BACKGROUND: Accurate prediction of successful sphincter-preserving resection (SSPR) for low rectal cancer enables peer institutions to scrutinize their own performance and potentially avoid unnecessary permanent colostomy. The aim of this study is to evaluate the variation in SSPR and present the first artificial intelligence (AI) models to predict SSPR in low rectal cancer patients. STUDY DESIGN: This was a retrospective post hoc analysis of a multicenter, non-inferiority randomized clinical trial (LASRE, NCT01899547) conducted in 22 tertiary hospitals across China. A total of 604 patients who underwent neoadjuvant chemoradiotherapy (CRT) followed by radical resection of low rectal cancer were included as the study cohort, which was then split into a training set (67%) and a testing set (33%). The primary end point of this post hoc analysis was SSPR, which was defined as meeting all the following criteria: (1) sphincter-preserving resection; (2) complete or nearly complete TME, (3) a clear CRM (distance between margin and tumour of 1 mm or more), and (4) a clear DRM (distance between margin and tumour of 1 mm or more). Seven AI algorithms, namely, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGB), light gradient boosting (LGB), decision tree classifier (DTC), random forest (RF) classifier, and multilayer perceptron (MLP), were employed to construct predictive models for SSPR. Evaluation of accuracy in the independent testing set included measures of discrimination, calibration, and clinical applicability. RESULTS: The SSPR rate for the entire cohort was 71.9% (434/604 patients). Significant variation in the rate of SSPR, ranging from 37.7 to 94.4%, was observed among the hospitals. The optimal set of selected features included tumour distance from the anal verge before and after CRT, the occurrence of clinical T downstaging, post-CRT weight and clinical N stage measured by magnetic resonance imaging. The seven different AI algorithms were developed and applied to the independent testing set. The LR, LGB, MLP and XGB models showed excellent discrimination with area under the receiver operating characteristic (AUROC) values of 0.825, 0.819, 0.819 and 0.805, respectively. The DTC, RF and SVM models had acceptable discrimination with AUROC values of 0.797, 0.766 and 0.744, respectively. LR and LGB showed the best discrimination, and all seven AI models had superior overall net benefits within the range of 0.3-0.8 threshold probabilities. Finally, we developed an online calculator based on the LGB model to facilitate clinical use. CONCLUSIONS: The rate of SSPR exhibits substantial variation, and the application of AI models has demonstrated the ability to predict SSPR for low rectal cancers with commendable accuracy.
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Canal Anal , Neoplasias Retais , Humanos , Neoplasias Retais/cirurgia , Neoplasias Retais/patologia , Neoplasias Retais/terapia , Masculino , Feminino , Pessoa de Meia-Idade , Estudos Retrospectivos , Canal Anal/cirurgia , Idoso , Inteligência Artificial , Tratamentos com Preservação do Órgão/métodos , Adulto , Terapia Neoadjuvante , ChinaRESUMO
Background: Radiation-induced colorectal fibrosis (RICF) is a common pathological alteration among patients with rectal cancer undergoing neoadjuvant chemoradiotherapy (nCRT). Anastomotic stenosis (AS) causes symptoms and negatively impacts patients' quality of life and long-term survival. In this study, we aimed to evaluate the fibrosis signature of RICF and develop a nomogram to predict the risk of AS in patients with rectal cancer undergoing nCRT. Methods: Overall, 335 pairs of proximal and distal margins were collected and randomly assigned at a 7:3 ratio to the training and testing cohorts. The RICF score was established to evaluate the fibrosis signature in the anastomotic margins. A nomogram based on the RICF score for AS was developed and evaluated by using the area under the curve, decision curve analysis, and the DeLong test. Results: The training cohort included 235 patients (161 males [68.51%]; mean age, 59.61 years) with an occurrence rate of AS of 17.4%, whereas the testing cohort included 100 patients (72 males [72.00%]; mean age, 57.17 years) with an occurrence rate of AS of 11%. The RICF total score of proximal and distal margins was significantly associated with AS (odds ratio, 3.064; 95% confidence interval [CI], 2.200-4.268; P < 0.001). Multivariable analysis revealed that the RICF total score, neoadjuvant radiotherapy, and surgical approach were independent predictors for AS. The nomogram demonstrated good discrimination in the training cohort (area under the receiver-operating characteristic curve, 0.876; 95% CI, 0.816-0.937), with a sensitivity of 68.3% (95% CI, 51.9%-81.9%) and a specificity of 85.5% (95% CI, 78.7%-89.3%). Similar results were observed in the testing cohort. Conclusions: This study results suggest that the RICF total score of anastomotic margins is an independent predictor for AS. The prediction model developed based on the RICF total score may be useful for individualized AS risk prediction in patients with rectal cancer undergoing nCRT and sphincter-preserving surgery.
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AIMS: This study aimed to investigate the effects of adipose-derived mesenchymal stem cells (ADSCs) on radiation-induced colorectal fibrosis (RICF) along with the associated dysbiosis of gut microbiota and metabolites. MAIN METHODS: Fecal microbiota were assessed through 16S rRNA gene sequencing, and the fecal metabolome was characterized using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. The correlation between microbiota and metabolome data was explored. KEY FINDINGS: ADSC injection demonstrated a significant restoration of radiation-induced intestinal damage in vivo. At the phylum level, irradiated rats exhibited an increase in Bacteroidota and Campilobacterota, and a decrease in Firmicutes and Desulfobacterota, contrasting with the ADSC treatment group. Metabolomic analysis revealed 72 differently expressed metabolites (DEMs) from gas chromatography-mass spectrometry and 284 DEMs from liquid chromatography-mass spectrometry in the radiation group compared to the blank group. In the ADSC treatment group versus the radiation group, 36 DEMs from gas chromatography-mass spectrometry and 341 DEMs from liquid chromatography-mass spectrometry were identified. KEGG enrichment analysis implicated pathways such as steroid hormone biosynthesis, gap junction, primary bile acid biosynthesis, citrate cycle, cAMP signaling pathway, and alanine, aspartate, and glutamate metabolism during RICF progression and after treated with ADSCs. Correlation analysis highlighted the role of ADSCs in modulating the metabolic process of Camelledionol in fecal Bacteroides. SIGNIFICANCE: These findings underscore the potential of ADSCs in reversing dysbiosis and restoring normal colonic flora in the context of RICF, offering valuable insights for therapeutic interventions targeting radiation-induced complications.