Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

Pei, Qian; Yi, Xiaoping; Chen, Chen; Pang, Peipei; Fu, Yan; Lei, Guangwu; Chen, Changyong; Tan, Fengbo; Gong, Guanghui; Li, Qingling; Zai, Hongyan; Chen, Bihong T.

doi:10.1007/s00330-021-08167-3

Cited by 44 publications

(46 citation statements)

References 37 publications

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“… 10 The radiomics nomogram incorporating radiomics signatures and clinical indicators of tumor location, patient age, high-density lipoprotein expression, and platelet counts could potentially be used to facilitate the individualized prediction of MSI status in patients with colorectal carcinoma. 11 To the best of our knowledge, there has been no machine learning research incorporating CT-based radiomics and clinicopathological variables to predict the MSI status of RC. Our integrated radiomics-clinicopathological nomogram showed better performance with AUCs of 0.843 (95% CI, 0.800–0.880) in the training set and 0.737 (95% CI, 0.659–0.805) in the validation set than the simple M-LR.…”

Section: Discussionmentioning

confidence: 99%

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A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Yuan¹,

Yu²,

Xu³

et al. 2022

CMAR

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Objective To predict the status of microsatellite instability (MSI) of rectal carcinoma (RC) using different machine learning algorithms based on tumoral and peritumoral radiomics combined with clinicopathological characteristics. Methods There were 497 RC patients enrolled in this retrospective study. The tumoral and peritumoral CT-based radiomic features were calculated after tumor segmentation. The radiomic features from two radiologists were compared by way of inter-observer correlation coefficient (ICC). After methods of variance, correlation, and dimension reduction, six machine learning algorithms of logistic regression (LR), Bayes, support vector machine, random forest, k-nearest neighbor, and decision tree were conducted to develop models for predicting MSI status of RC. The relative standard deviation (RSD) was quantified. The radiomics and significant clinicopathological variables constituted the radiomics-clinicopathological nomogram. The receiver operator curve (ROC) was made by DeLong test, and the area under curve (AUC) with 95% confidence interval (95% CI) was calculated to evaluate the performance of the model. Results The venous phase of CT examination was selected for further analysis because the proportion of radiomic features with ICC greater than 0.75 was higher. The tumoral and peritumoral model by LR algorithm (M-LR) with minimal RSD showed good performance in predicting MSI status of RC with the AUCs of 0.817 and 0.726 in the training and validation set. The radiomic-clinicopathological nomogram performed better in both the training and validation set with AUCs of 0.843 and 0.737. Conclusion The radiomics-clinicopathological nomogram demonstrated better predictive performance in evaluating the MSI status of RC.

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Section: Discussionmentioning

confidence: 99%

“… 10 The clinical-radiomics nomogram illustrated that radiomic features, tumor location, age, high-density lipoprotein expression, and platelet counts showed good performance in assessing MSI status of colorectal cancer. 11 …”

Section: Introductionmentioning

confidence: 99%

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Yuan¹,

Yu²,

Xu³

et al. 2022

CMAR

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“…Max-Relevance and Min-Redundancy (mRMR) was performed to eliminate the redundant and irrelevant features ( 31 ), and 20 features were retained. Then, the least absolute shrinkage and selection operator (LASSO) method was used to select the optimized subset of features to construct the radiomic signature (Rad-score) and to build the models.…”

Section: Methodsmentioning

confidence: 99%

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma

Wang

et al. 2021

Front. Oncol.

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PurposeEarly recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.Patients and MethodsA total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.ResultsThe nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.ConclusionThis study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

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“…Establishing tumour MMR status earlier in the diagnostic pathway could accelerate LS detection and enhance patient care. A number of recent analyses suggest that the application of machine learning to computed tomography [ 15 , 16 , 17 ] or infrared [ 18 , 19 , 20 ] imaging may have the potential to discriminate microsatellite stable (MSS) from microsatellite unstable (MSI-High/MSI-H) tumours. However, such methods are not routinely available to all patients, and have many competing demands for their use.…”

Section: Introductionmentioning

confidence: 99%

Detection of Microsatellite Instability in Colonoscopic Biopsies and Postal Urine Samples from Lynch Syndrome Cancer Patients Using a Multiplex PCR Assay

Phelps

Gallon

Hayes

et al. 2022

Cancers

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Identification of mismatch repair (MMR)-deficient colorectal cancers (CRCs) is recommended for Lynch syndrome (LS) screening, and supports targeting of immune checkpoint inhibitors. Microsatellite instability (MSI) analysis is commonly used to test for MMR deficiency. Testing biopsies prior to tumour resection can inform surgical and therapeutic decisions, but can be limited by DNA quantity. MSI analysis of voided urine could also provide much needed surveillance for genitourinary tract cancers in LS. Here, we reconfigure an existing molecular inversion probe-based MSI and BRAF c.1799T > A assay to a multiplex PCR (mPCR) format, and demonstrate that it can sample >140 unique molecules per marker from <1 ng of DNA and classify CRCs with 96–100% sensitivity and specificity. We also show that it can detect increased MSI within individual and composite CRC biopsies from LS patients, and within preoperative urine cell free DNA (cfDNA) from two LS patients, one with an upper tract urothelial cancer, the other an undiagnosed endometrial cancer. Approximately 60–70% of the urine cfDNAs were tumour-derived. Our results suggest that mPCR sequence-based analysis of MSI and mutation hotspots in CRC biopsies could facilitate presurgery decision making, and could enable postal-based screening for urinary tract and endometrial tumours in LS patients.

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Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

Cited by 44 publications

References 37 publications

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma

Detection of Microsatellite Instability in Colonoscopic Biopsies and Postal Urine Samples from Lynch Syndrome Cancer Patients Using a Multiplex PCR Assay

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