Total tumour volume as a prognostic factor in patients with resectable colorectal cancer liver metastases

Tai, Katherine Yuxi; Komatsu, Shigeru; Sofue, Keitarou; Kido, Masahiro; Tanaka, Mari; Kuramitsu, Kaori; Awazu, Masafumi; Gon, Hidetoshi; Tsugawa, Daisuke; Yanagimoto, H.; Toyama, Hirochika; Murakami, Shuji; Murakami, Takamichi; Fukumoto, Tetsuya

doi:10.1002/bjs5.50280

Cited by 11 publications

(8 citation statements)

References 38 publications

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“…The volumetric parameters, including MTV and TLG, are able to assess metabolic activity and measure the volumetric burden of tumor simultaneously. It is well known that the tumor volume alone is a robust prognostic factor (39,40). Hence, the prognostic value of MTV and TLG are enhanced by additional measurement of tumor volume.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of 18F-FDG PET/CT in malignant pleural mesothelioma: a meta-analysis

Wang

Kan

et al. 2022

Acta Radiol

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Background Several FDG PET/CT parameters have been utilized to evaluate the prognosis in malignant pleural mesothelioma (MPM). However, there are still controversial results due to the low incidence of MPM. Purpose To assess the prognostic value of 18F-FDG PET/CT in MPM. Material and Methods A systematic literature search was performed in PubMed, Embase, Medline, and The Cochrane Library to identify eligible studies from inception to 12 February 2020. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) of several variables, such as maximum standardized uptake value (SUVmax), the reduction of SUVmax after treatment (ΔSUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and the reduction of TLG after treatment (ΔTLG), were calculated. Meta-regression with subsequent subgroup analyses were conducted to determine the heterogeneity of cutoff values, treatment regimen, study design, uptake time, and scanners across various studies. Results In total, 19 eligible studies including 1819 patients were enrolled in the meta-analysis. The univariate analysis showed that the pooled HRs (95% CI) of SUVmax, ΔSUVmax, MTV, TLG, and ΔTLG were 1.29 (1.16–1.42), 1.12 (1.05–1.19), 1.15 (1.00–1.33), 1.47 (1.23–1.76), and 1.27 (1.12–1.45), respectively. The multivariate analysis showed that the pooled HRs (95% CI) of SUVmax, ΔSUVmax, MTV, and TLG for overall survival (OS) were 1.20 (1.08–1.33), 1.10 (1.02–1.19), 0.95 (0.81–1.11), and 1.13 (1.08–1.18), respectively. Conclusion SUVmax, ΔSUVmax, TLG, and ΔTLG are significant prognostic indicators for OS, while more clinical studies are needed to confirm the prognostic value of MTV in MPM.

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Section: Discussionmentioning

confidence: 99%

Prognostic value of 18F-FDG PET/CT in malignant pleural mesothelioma: a meta-analysis

Wang

Kan

et al. 2022

Acta Radiol

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“…The above keywords may be the focus of researchers in this field. Among them, CT had the highest number of occurrences and also had a high centrality (0.25), which might be because most 3D technologies were based on CT images [25,35,[48][49][50]. The possible reason for the high centrality of the anatomy (0.25) is that providing more accurate anatomical information is one of the significant advantages of 3D technology [32,37,39].…”

Section: Abstract Analysismentioning

confidence: 99%

A Bibliometric Analysis of the Role of 3D Technology in Liver Cancer Resection

et al. 2023

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Background Liver cancer resection is an effective but complex way to treat liver cancer, and complex anatomy is one of the reasons for the difficulty of surgery. The use of 3D technology can help surgeons cope with this dilemma. This article intends to conduct a bibliometric analysis of the role of 3D technology in liver cancer resection. Methods (TS = (3D) OR TS = (three‐dimensional)) AND (TS = (((hepatic) OR (liver)) AND ((cancer) OR (tumor) OR (neoplasm)))) AND (TS = (excision) OR TS = (resection)) was used as a search strategy for data collection in the Web of Science (WoS) Core Collection. CiteSpace, Carrot2 and Microsoft Office Excel were used for data analysis. Results Three hundred and eighty‐eight relevant articles were obtained. Their annual and journal distribution maps were produced. Countries/regions and institutions collaboration, author collaboration, references co‐citations and their clusters and keywords co‐occurrences and their clusters were constructed. Carrot2 cluster analysis was performed. Conclusions There was an overall upward trend in the number of publications. China's contribution was greater, and the USA had greater influence. Southern Med Univ was the most influential institution. However, the cooperation between institutions still needs to be further strengthened. Surgical Endoscopy and Other Interventional Techniques was the most published journal. Couinaud C and Soyer P were the authors with the highest citations and centrality, respectively. “Liver planning software accurately predicts postoperative liver volume and measures early regeneration” was the most influential article. 3D printing, 3D CT and 3D reconstruction may be the mainstream of current research, and augmented reality (AR) may be a future hot spot.

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“…By doing so, the effects of systemic therapy could be underestimated by RECIST1.1, as it ignores potentially valuable information about total tumor volume (TTV). Assessment of TTV response to systemic therapy could represent a clinically more reliable evaluation since baseline TTV has shown to be prognostic for overall survival and change in TTV for recurrence-free survival in patients with CRLM, whereas RECIST1.1 has not [ 6 , 7 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

Wesdorp,

Zeeuw,

Postma

et al. 2023

Eur Radiol Exp

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Background We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation. Conclusions Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency. Key points • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations. Graphical Abstract

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Total tumour volume as a prognostic factor in patients with resectable colorectal cancer liver metastases

Cited by 11 publications

References 38 publications

Prognostic value of 18F-FDG PET/CT in malignant pleural mesothelioma: a meta-analysis

Prognostic value of 18F-FDG PET/CT in malignant pleural mesothelioma: a meta-analysis

A Bibliometric Analysis of the Role of 3D Technology in Liver Cancer Resection

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

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