Significance of Glisson's capsule invasion in patients with colorectal liver metastases undergoing resection

Hiroyoshi, Junko; Arita, Junichi; Gonoi, Wataru; Akamatsu, Nobuhisa; Kaneko, Junichi; Hasegawa, Kiyoshi

doi:10.1016/j.amjsurg.2019.01.023

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…If a predicted positive surgical margin after resection is no longer an absolute contraindication to surgery for treating advanced and aggressive liver metastases[18], tumor size reduction by>60% could permit resection that preserves the vessel showing attachment, without vascular resection or reconstruction. In such situations, liver metastases attached to or invading major intrahepatic vessels seemed not easily separable by treatment, even with a regimen including monoclonal antibodies [19,20]. Extent of tumor attachment to the vessels and deformity of the vessels on CT were reported to be useful indications for concomitant liver and vessel resection, focusing on hepatic caval invasion of the liver tumors [21].…”

Section: Discussionmentioning

confidence: 99%

Tumor attached to major intrahepatic vascular for colorectal liver metastases after preoperative chemotherapy

Liu

Cui

et al. 2023

Preprint

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Background: Colorectal liver metastases attached major intrahepatic vessels has been considered to be risk factor for survival outcome after liver resection. The present study aimed to clarify the outcomes of R1 surgery (margin<1mm) in CRLM patients, distinguishing parenchymal margin R1 and attached to major intrahepatic vessels R1. Methods: In the present study, 283 CRLM patients who evaluated to be attached to major intrahepatic vessels initially, underwent liver resection following preoperative chemotherapy. They were assigned to one of the following groups: R0 (n=167), R1 parenchymal (n=58) and R1 vascular (n=58). We analyzed the survival outcomes and local recurrence rates in each group. Results: Overall, 3- and 5-year OS rates after liver resection were 53.0% and 38.2% (median OS 37 months). Five-year OS was higher in patients with R0 than R1p (44.9%% vs. 26.3%, p=0.009), whereas there was no significant difference from patients with R1v (34.3%, p=0.752). In the multivariable analysis, preoperative chemotherapy>4 cycles, CRS 3-5, RAS mutation, R1p and CA199>100IU/ml were identified as independent predictive factors of OS (p<0.05). There was no significant difference for local recurrence among three groups. Conclusion: R1 parenchymal resection was independent risk factor for CRLM. R1 vascular surgery achieves outcomes equivalent to R0 resection. Non-anatomic liver resection for CRLM attached to intrahepatic vessels can be pursued to increase patient resectability by preoperative chemotherapy.

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Section: Discussionmentioning

confidence: 99%

Tumor attached to major intrahepatic vascular for colorectal liver metastases after preoperative chemotherapy

Liu

Cui

et al. 2023

Preprint

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“…This new diagnostic system could be effective in intraoperative evaluation of surgical margin, lymph node metastasis and disappearing CRLM after chemotherapy. CRLM frequently invades intrahepatic vascular structures, which could affect the postoperative prognosis [4,18]. However, a surgical margin is sometimes judged negative at intraoperative examination using frozen section although postoperative examination using paraffinembedded sections did positive [19].…”

Section: Discussionmentioning

confidence: 99%

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis

et al. 2021

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Background Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM). Methods A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail. Results This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P < 0.01, respectively). The proportion of phospholipids categorized as monounsaturated fatty acids was higher in CRLM (37.2%) than in non-cancer liver parenchyma (10.7%; P < 0.01). Conclusion The combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM.

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“…CRLM frequently invades intrahepatic vascular structures, which could affect the postoperative prognosis. (4,16) However, a surgical margin is sometimes judged negative at intraoperative examination using frozen section although postoperative examination using para n-embedded sections did positive. (17) Similarly, a small foci of cancerous cells in a resected lymph node is sometimes con rmed only after operation.…”

Section: Discussionmentioning

confidence: 99%

A New Rapid Diagnostic System with Ambient Mass Spectrometry and Machine Learning for Colorectal Liver Metastasis

Kiritani

Yoshimura

Arita

et al. 2020

Preprint

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Background: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM).Methods: A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail.Results: This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P<0.01, respectively). The proportion of phospholipids categorized as monounsaturated fatty acids was higher in CRLM (37.2%) than in non-cancer liver parenchyma (10.7%; P<0.01).Conclusion: The combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM.

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Significance of Glisson's capsule invasion in patients with colorectal liver metastases undergoing resection

Cited by 9 publications

References 21 publications

Tumor attached to major intrahepatic vascular for colorectal liver metastases after preoperative chemotherapy

Tumor attached to major intrahepatic vascular for colorectal liver metastases after preoperative chemotherapy

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis

A New Rapid Diagnostic System with Ambient Mass Spectrometry and Machine Learning for Colorectal Liver Metastasis

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