The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment

Wesdorp, Nina J.; Bolhuis, Karen; Roor, Joran; Waesberghe, Jan-Hein T M van; Amerongen, Martin J. van; Chapelle, T.; Dejong, Cornelis H. C.; Engelbrecht, Marc R.; Gerhards, Michael F.; Grünhagen, Dirk J.; Gulik, Thomas M. van; Hermans, John J.; Jong, Koert P. de; Klaase, Joost M.; Liem, Mike S L; Lienden, Krijn P. van; Molenaar, I. Quintus; Patijn, Gijs A.; Rijken, Arjen M.; Ruers, Theo; Verhoef, Cornelis; Wilt, Johannes H. W. de; Swijnenburg, Rutger-Jan; Punt, Cornelis J.A.; Huiskens, Joost; Kazemier, Geert

doi:10.1097/as9.0000000000000103

Cited by 5 publications

(7 citation statements)

References 47 publications

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“…The summary statistics were calculated with formulas proposed by LiTS [ 25 ]. Total tumor volume was calculated in the SAS Viya® Analytical platform using the quantifyBioMedImages action [ 7 , 34 ]. Total tumor volume was determined as the product of the voxel volume and the number of segmented voxels of all CRLM present in the liver and was reported as a continuous variable in cubic centimeters.…”

Section: Methodsmentioning

confidence: 99%

“…In patients with colorectal liver metastases (CRLM), the efficacy of RECIST1.1 has been questioned [ 5 – 7 ]. Colorectal cancer is the third most common cancer and the second leading cause of cancer-related deaths for men and women globally [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…In recent years, several studies demonstrated that volumetric assessment using algorithms increases the reproducibility of response assessments [ 6 , 20 – 22 ]. In most of these studies, semiautomatic segmentation models are used to perform volumetric assessments [ 6 , 7 , 20 – 22 ]. Segmentation is the delineation of tissue structures on diagnostic imaging, resulting in 3D contours of these structures.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…1. Applying Response Evaluation Criteria for Solid Tumors (RECIST 1.1) criteria to solid tumors involves measuring the largest diameter of a tumor, but tumor volume and morphology give a more comprehensive assessment of treatment response,4,5 so there is an opportunity to improve RECIST 1.1 with AI, ML, and DL 6. AI can determine volumetric changes in the three-dimensional morphology of cancers that are not simple spherical or elliptical structures, while eliminating subjectivity and observer variability7-10 and reducing time assessing tumor response.…”

Section: Four Key Themes From the Summitmentioning

confidence: 99%

Artificial Intelligence Comes of Age in the Fight Against Cancer

Yu¹

2022

Oncology Times

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The SAS Virtual Health Artificial Intelligence (AI) Summit on Cancer Research 1 was held this year to share best practices, ongoing challenges, and future opportunities for advancing cancer treatment through analytics. Innovations in applying computer vision to medical images and using machine learning (ML) to build predictive models may help clinicians assess therapeutic results more efficiently, thereby enhancing personalized approaches to cancer treatment.AI is the application of digital devices and computers to enhance human intelligence. 2 In this article, we focus on the use of AI to develop ML and deep learning (DL) models. Whereas ML is the subfield of AI using mathematical and statistical approaches to derive models from data, DL is a specific class of ML that leverages complex networks in its learning process (Figure 1). 3

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Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation

Michiel Zeeuw,

Wesdorp,

Ali

et al. 2024

European Journal of Cancer

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The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment

Cited by 5 publications

References 47 publications

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

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

Artificial Intelligence Comes of Age in the Fight Against Cancer

Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation

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