Are positron emission tomography-computed tomography (PET-CT) scans useful in preoperative assessment of patients with peritoneal disease before cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC)?

Wang, Weining; Tan, Grace Hwei Ching; Chia, Claramae Shulyn; Skanthakumar, Thakshayeni; Soo, Khee Chee; Teo, Melissa Ching Ching

doi:10.1080/02656736.2017.1366554

Cited by 12 publications

(2 citation statements)

References 32 publications

(43 reference statements)

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“…This may be attributed to the rich tissue metabolism information in the original image, but a low number of pixels and the lack of anatomical texture information in the image. These results align with recent research, indicating a close relationship between FDG metabolism levels in primary tumor tissue and CRPM, with mucus components prone to causing PM exhibiting lower FDG uptake, although the AUC value was relatively low in this study [ 9 , 46 , 47 ].…”

Section: Discussionsupporting

confidence: 93%

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

Zhang,

Zheng,

et al. 2024

Insights Imaging

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Objectives Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. Methods A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). Results The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874–0.978), 0.897 (95% CI: 0.801–0.994), 0.885 (95% CI: 0.795–0.975), and 0.889 (95% CI: 0.823–0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. Conclusions The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. Critical relevance statement The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. Key Points Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients. Graphical Abstract

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Section: Discussionsupporting

confidence: 93%

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

Zhang,

Zheng,

et al. 2024

Insights Imaging

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“…It has a higher interobserver reproduci bility than CT and helps in selecting potential candidates for CRS by excluding extra abdominal disease. However, PET-CT has a longer acquisition time than CT and underperforms in mucinous PSM 165 .…”

Section: Spiral Ctmentioning

confidence: 99%

Primary and metastatic peritoneal surface malignancies

2021

Nat Rev Dis Primers

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Peritoneal surface malignancies comprise a heterogeneous group of primary tumours, including peritoneal mesothelioma, and peritoneal metastases of other tumours, including ovarian, gastric, colorectal, appendicular or pancreatic cancers. The pathophysiology of peritoneal malignancy is complex and not fully understood. The two main hypotheses are the transformation of mesothelial cells (peritoneal primary tumour) and shedding of cells from a primary tumour with implantation of cells in the peritoneal cavity (peritoneal metastasis). Diagnosis is challenging and often requires modern imaging and interventional techniques, including surgical exploration. In the past decade, new treatments and multimodal strategies helped to improve patient survival and quality of life and the premise that peritoneal malignancies are fatal diseases has been dismissed as management strategies, including complete cytoreductive surgery embedded in perioperative systemic chemotherapy, can provide cure in selected patients. Furthermore, intraperitoneal chemotherapy has become an important part of combination treatments. Improving locoregional treatment delivery to enhance penetration to tumour nodules and reduce systemic uptake is one of the most active research areas. The current main challenges involve not only offering the best treatment option and developing intraperitoneal therapies that are equivalent to current systemic therapies but also defining the optimal treatment sequence according to primary tumour, disease extent and patient preferences. New imaging modalities, less invasive surgery, nanomedicines and targeted therapies are the basis for a new era of intraperitoneal therapy and are beginning to show encouraging outcomes.

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Hipec

Ghabra,

Blakely,

Mannes

et al. 2023

Anesthesia for Oncological Surgery

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Are positron emission tomography-computed tomography (PET-CT) scans useful in preoperative assessment of patients with peritoneal disease before cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC)?

Abstract: Our results suggest that PET-CT can be used as an adjunct to CT and/or MRI scans, when lesions on the CT/MRI scans are indeterminate, and that it is most useful in patients with non-mucinous tumours.

Cited by 12 publications

References 32 publications

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

Primary and metastatic peritoneal surface malignancies

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