Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort

Atre, Isha D; Eurboonyanun, Kulyada; Noda, Yoshifumi; Parakh, Anushri; O’Shea, Aileen; Lahoud, Rita Maria; Sell, Naomi M.; Kunitake, Hiroko; Harisinghani, Mukesh G.

doi:10.1007/s00261-020-02653-w

Cited by 10 publications

(10 citation statements)

References 24 publications

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“…For the diagnosis of TD, a recent retrospective study indicated that preoperative MRI with the incorporation of texture analysis parameters, morphological parameters, and lesion shape was helpful in differentiating TD from LNM. However, this kind of MRI can only recognize TD with size > 1 cm and EMVI ( 19 ), and the availability limited their usage in clinical practice. At present, pathology is still the main way to diagnose TD.…”

Section: Introductionmentioning

confidence: 99%

Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

Chen

Zhang

et al. 2022

Front. Oncol.

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BackgroundMore and more evidence indicated that tumor deposit (TD) was significantly associated with local recurrence, distant metastasis (DM), and poor prognosis for patients with colorectal cancer (CRC). This study aims to explore the main clinical risk factors for the presence of TD in CRC patients with no DM (CRC-NDM) and the prognostic factors for TD-positive patients after surgery.MethodsThe data of patients with CRC-NDM between 2010 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A logistic regression model was used to identify risk factors for TD presence. Fine and Gray’s competing-risk model was performed to analyze prognostic factors for TD-positive CRC-NDM patients. A predictive nomogram was constructed using the multivariate logistic regression model. The concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration were used to evaluate the predictive nomogram. Also, a prognostic nomogram was built based on multivariate competing-risk regression. C-index, the calibration, and decision-curve analysis (DCA) were performed to validate the prognostic model.ResultsThe predictive nomogram to predict the presence of TD had a C-index of 0.785 and AUC of 0.787 and 0.782 in the training and validation sets, respectively. From the competing-risk analysis, chemotherapy (subdistribution hazard ratio (SHR) = 0.542, p < 0.001) can significantly reduce CRC-specific death (CCSD). The prognostic nomogram for the outcome prediction in postoperative CRC-NDM patients with TD had a C-index of 0.727. The 5-year survival of CCSD was 17.16%, 36.20%, and 63.19% in low-, medium-, and high-risk subgroups, respectively (Gray’s test, p < 0.001).ConclusionsWe constructed an easily predictive nomogram in identifying the high-risk TD-positive CRC-NDM patients. Besides, a prognostic nomogram was built to help clinicians identify poor-outcome individuals in postoperative CRC-NDM patients with TD. For the high-risk or medium-risk subgroup, additional chemotherapy may be more advantageous for the TD-positive patients rather than radiotherapy.

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Section: Introductionmentioning

confidence: 99%

Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

Chen

Zhang

et al. 2022

Front. Oncol.

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Section: Discussionmentioning

confidence: 99%

“…Despite some techniques like MRI texture analysis or phenotype determinations that have been introduced to roughly assess the TDs status, an accurate prediction shaping personalized TDs features remains an arduous task. [31][32][33][34] A predictive nomogram is currently an efficient tool to improve preoperative TDs prediction and investigate related mechanisms. Our nomogram incorporated six-gene risk score system (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) and exhibited satisfactory performances in high-risk group identification.…”

Section: Discussionmentioning

confidence: 99%

A Six-microRNA Signature Nomogram for Preoperative Prediction of Tumor Deposits in Colorectal Cancer

Xiao

Guo

Zhang

et al. 2022

IJGM

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Purpose Tumor deposits (TDs) are acknowledged negative prognostic factors in colorectal cancer (CRC), and their pathogenesis remains a puzzle. This study aimed to construct and validate a nomogram available for preoperative TDs prediction in CRC patients. Patients and Methods Patients from the Surveillance, Epidemiology, and End Results (SEER) and the cancer genome atlas (TCGA) databases were randomly divided into training and validation sets according to the sample size ratio of 7:3. Univariate logistic regression was performed for identifying differentially expressed microRNAs between TDs and non-TDs. Nomograms for TDs prediction were developed from the multivariate logistic regression model with least absolute shrinkage and selection operator and were validated internally in terms of accuracy, calibration, and clinical utility. Based on the target genes, pathways tightly associated with TDs were selected using enrichment analysis. Results Six clinicopathologic factors and expressions of six microRNAs (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) differed significantly between TDs and non-TDs CRC patients from the SEER and TCGA training sets. We compared potential prediction discrimination between two nomograms: a clinicopathologic nomogram and a six-microRNA signature nomogram. The six-microRNA signature nomogram revealed better accuracy than the clinicopathologic one for TDs prediction (AUC values of 0.96 and 0.93 in the validation cohort). The calibration plots and decision curve analysis demonstrated that the six-microRNA signature nomogram had better validity and a greater prognostic benefit versus the clinicopathologic one for TDs prediction. Calcium signaling pathways were closely associated with roles of the six microRNAs in TDs of CRC patients. Conclusion The six-microRNA signature nomogram can be used as an efficient tool for preoperative TDs prediction in CRC patients.

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“…Among patients with LNM, the occurrence of TDs can lead to a worse prognosis[ 4 , 5 ], strongly indicating that their impact on prognosis is independent and additive. Therefore, TDs must be reported separately from LNM[ 6 ]. However, TDs and LNM can only be determined through pathological examinations of surgical specimens[ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…These features can capture characteristics of volume of interest (VOI), such as heterogeneity, and it may, alone or in combination with other data, be used to solve clinical problems[ 8 ]. Recently, some studies have reported the involvement of TDs in RC[ 6 , 9 , 10 ]. However, some previous studies did not focus on the differentiation of TDs from LNM[ 9 , 10 ], and Atre et al [ 6 ] used only texture analysis and lacked the construction and validation of a model.…”

Section: Introductionmentioning

confidence: 99%

Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer

Zhang¹,

Li²,

Jin³

et al. 2022

WJG

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BACKGROUND Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task. AIM To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC. METHODS This study retrospectively enrolled 219 patients with RC [TDs + LNM - ( n = 89); LNM + TDs - ( n = 115); TDs + LNM + ( n = 15)] from a single center between September 2016 and September 2021. Single-positive patients ( i.e. , TDs + LNM - and LNM + TDs - ) were classified into the training ( n = 163) and validation ( n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients ( i.e. , TDs + LNM + ) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm. RESULTS Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 ( P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs + LNM + ( n = 15); single-positive ( n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%). ...

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Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort

Cited by 10 publications

References 24 publications

Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

A Six-microRNA Signature Nomogram for Preoperative Prediction of Tumor Deposits in Colorectal Cancer

Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer

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