Editor's Challenge: Abhishek Mahajan - How Can Precision Oncology be Advanced with Validated Imaging-Based Nomograms?

Editorial

01 February 2024

Editorial: Editor’s challenge: Abhishek Mahajan - how can precision oncology be advanced with validated imaging-based nomograms?

Abhishek Mahajan

Shreya Shukla

Richa Vaish

and

Manish Devendra Mair

1,137 views

0 citations

Editors

Abhishek Mahajan

The Clatterbridge Cancer Centre

Manish Devendra Mair

University Hospitals of Leicester NHS Trust

Impact

Original Research

23 October 2023

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Yuan Li

, 6 more and

Fei-xue Zhang

Objective: To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.

Materials and methods: A total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed.

Result: There was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets.

Conclusion: SA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.

3,295 views

4 citations

Original Research

12 October 2023

A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma

Yahan Tong

, 7 more and

Fangxiao Lu

Purpose: To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.

Materials and methods: The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results: We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.

Conclusion: We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

1,921 views

4 citations

(A, C) the x-axis represents the nomogram-predicted probability and the y-axis represents the actual probability of PDM and pLN. The perfect prediction corresponds to the 45° dotted line. The red solid line represents the entire cohort (n=114), and the blue solid line is the bias-corrected value by bootstrapping (B=1000 repetitions), indicating the observed nomogram performance. The calibration curve showed an evident relationship between the actual tag and the predicted tag. (B) the area under the ROC curve of the nomogram for predicting PDM was 0.837, suggesting that the confidence level of the probability of distant metastasis predicted by this nomogram was 83.7%. (D) the area under the ROC curve of the nomogram for predicting pLN was 0.817, suggesting that the confidence level of the probability of pelvic lymph node metastasis by this nomogram was 81.7%.

Original Research

21 August 2023

Predictive value of MRI-detected tumor deposits in locally advanced rectal cancer

Baohua Lv

, 3 more and

Erhu Jin

1,734 views

2 citations

Original Research

28 February 2023

Development and validation of a MRI-based combined radiomics nomogram for differentiation in chondrosarcoma

Xiaofen Li

, 8 more and

Wentao Dong

Objective: This study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma.

Methods: A total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness.

Results: Using multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value.

Conclusion: The MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

1,626 views

8 citations

87. Smooth tumor margin and TLRHBP was 0.68 (A). T1HBP was 682 ms (B). The tumor had low Ki-67 LI (10%, IHC ×400) (C). A 65-year-old male with elevated AFP (124 ng/mL) and NLR 2.35. Non-smooth tumor margin and TLRHBP was 0.55 (A). T1HBP was 786 ms (B). The tumor had high Ki-67 LI (40%, IHC ×400) (C). AFP, alpha-fetoprotein; NLR, neutrophils to lymphocyte ratio; TLRHBP, tumor-to-liver contrast ratio in the hepatobiliary phase; T1HBP, T1 relaxation time on the hepatobiliary phase; LI, labeling index; IHC, immunohistochemistry.

Original Research

30 June 2023

Gadoxetic acid-enhanced MRI combined with T1 mapping and clinical factors to predict Ki-67 expression of hepatocellular carcinoma

Ganbin Qiu

, 4 more and

Yue Zhao

2,115 views

1 citations

Original Research

25 November 2022

Clinic friendly estimation of muscle composition: Preoperative linear segmentation shows overall survival correlated with muscle mass in patients with nonmetastatic renal cell carcinoma

Benjamin N. Schmeusser

, 13 more and

Viraj A. Master

Purpose: Sarcopenia is associated with decreased survival and increased complications in patients with renal cell carcinoma. Readily identifying patients with low muscle composition that may experience worse outcomes or would benefit from preoperative intervention is of clinical interest. Traditional body composition analysis methods are resource intensive; therefore, linear segmentation with routine imaging has been proposed as a clinically practical alternative. This study assesses linear segmentation’s prognostic utility in nonmetastatic renal cell carcinoma.

Materials and Methods: A single institution retrospective analysis of patients that underwent nephrectomy for nonmetastatic renal cell carcinoma from 2005-2021 was conducted. Linear segmentation of the bilateral psoas/paraspinal muscles was completed on preoperative imaging. Total muscle area and total muscle index associations with overall survival were determined by multivariable analysis.

Results: 532 (388 clear cell) patients were analyzed, with median (IQR) total muscle index of 28.6cm²/m² (25.8-32.5) for women and 33.3cm²/m² (29.1-36.9) for men. Low total muscle index was associated with decreased survival (HR=1.96, 95% CI 1.32-2.90, p<0.001). Graded increases in total muscle index were associated with better survival (HR=0.95, 95% CI 0.92-0.99, p=0.006).

Conclusions: Linear segmentation, a clinically feasible technique to assess muscle composition, has prognostic utility in patients with localized renal cell carcinoma, allowing for incorporation of muscle composition analysis into clinical decision-making. Muscle mass determined by linear segmentation was associated with overall survival in patients with nonmetastatic renal cell carcinoma.

2,521 views

5 citations