Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma

Chu, Hongpeng; Liu, Zelong; Wen, Liang; Zhou, Qian; Zhang, Ying; Lei, Kai; Tang, Mimi; Cao, Yi-heng; Chen, Shu‐Ling; Peng, Sui; Kuang, Ming

doi:10.1007/s00330-020-07250-5

Cited by 34 publications

(37 citation statements)

References 22 publications

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“…Both Ji and Chu reported excellent interobserver agreement, yet they both analyzed only experienced readers. In particular, Chu reported excellent agreement (98.7% RF with ICC > 0.5) [23], which is substantially different from our results showing higher variability between one resident and one experience abdominal radiologist (71% RF with ICC > 0.5). Such discrepancy underscores the need for experienced reader for high-skilled segmentation of tumor boundaries.…”

Section: Variability and Selection Of Rfcontrasting

confidence: 99%

“…Nonetheless, "Surface volume ratio" relies on manual segmentation with as low as ICC 0.54 and therefore underscores the relevance of standardized segmentation. The six selected RF were similar in kind (first-order RF including percentiles of density) to those from a previous study focusing on prediction of surgical utility by RF in portal venous In these two cases, the inclusion of RSign (Model 2) improved the stratification of 1-year survival phase CT [23]. This overlapping character (portal venous phase and first-order radiomics) suggests that relatively simple RF should be deemed relevant also in an unselected population like ours (including both surgery candidates and advanced disease).…”

Section: Variability and Selection Of Rfmentioning

confidence: 93%

“…This study included a broad population with full range of treatment options, consistent with ours. In 2020, Chu reported that RF could stratify poor outcome after surgery [23]. Both Ji and Chu reported excellent interobserver agreement, yet they both analyzed only experienced readers.…”

Section: Variability and Selection Of Rfmentioning

confidence: 99%

“…Accurate non-invasive prognostic descriptors of IMCC are deemed of major clinical support for personalized therapeutic approach because IMCC is one form of intrahepatic cholangiocarcinoma for surgical option, and therefore radiomics might represent a relevant complement in pre-surgical clinical management. Most of the published research focused on population selected by clinical treatment, namely surgery or embolization [23,24]. However, the application of radiomics on broader population is still lacking, especially for management throughout the full process of treatment decision.…”

Section: Introductionmentioning

confidence: 99%

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Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

et al. 2021

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Purpose To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model. Methods Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared. Results Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2. Conclusion The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

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Section: Variability and Selection Of Rfcontrasting

confidence: 99%

Section: Variability and Selection Of Rfmentioning

confidence: 93%

Section: Variability and Selection Of Rfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

et al. 2021

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“…However, they may not cover the clinical complexity because they are all based only on a few, mainly tumor burden-associated factors. Knowledge about novel risk factors, such as the tumor microenvironment, the influence of inflammation and immune reactions, body composition assessment, tumor standardized uptake in hybrid positron emission tomography/computed tomography imaging, and image-based texture analysis has continuously been increasing [ 25 , 32 , 33 , 34 , 35 , 36 , 37 ]. Therefore, the integration of these factors into scoring systems has great potential.…”

Section: Discussionmentioning

confidence: 99%

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Müller

Mähringer‐Kunz

Gairing

et al. 2021

JCM

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Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.

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Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

et al. 2022

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Objective To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. Methods Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders’ assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. Results From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, −0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0–30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. Conclusions The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. Key Points • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, −0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.

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Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma

Cited by 34 publications

References 22 publications

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

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