A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules

Radiomic score for lung nodules as a prognostic biomarker in locally advanced rectal cancer patients: A bi-institutional study

BACKGROUND

Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic-based score (Rad-score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules.

METHODS

Retrospective data from two institutions (July 10, 2006-September 24, 2015) was used to develop and validate the Rad-score for distinguishing lung nodule malignancy. The prognostic value of the Rad-score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli-Rad-score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results.

RESULTS

The Rad-score demonstrated great performance in distinguishing malignancy, with C-index values of 0.793 [95% CI: 0.729-0.856] in the training set and 0.730 [95% CI: 0.666-0.874] in the validation set. In independent LARC cohorts, Rad-score validation achieved C-index values of 0.794 [95% CI: 0.737-0.851] and 0.747 [95% CI: 0.615-0.879]. Regarding prognostic prediction, Rad-score effectively stratified patients. Cli-Rad-score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C-index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set).

CONCLUSIONS

CT-based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad-score predicts prognosis independently. Incorporation of Cli-Rad-score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

A prediction model for lung metastases in patients with indeterminate pulmonary nodules in newly diagnosed colorectal cancer

INTRODUCTION

Multidisciplinary teams treating patients with newly diagnosed Colorectal Cancer (CRC) often encounter the appearance of Indeterminate Pulmonary Nodules (IPNs) that warrants follow-up with repetitive medical imaging and anxiety for patients. We determined the incidence of IPNs in patients with newly diagnosed CRC and developed and validated a model for individualized risk prediction of IPNs being lung metastases.

MATERIAL AND METHODS

Newly diagnosed CRC who underwent surgery between November 2011 to June 2014 were included to create the risk model, developed using both clinical experience and statistical selection. Discrimination and calibration slopes of the risk score were evaluated in an independent temporal validation sample. A nomogram is presented to assist clinicians in estimating an individual risk score.

RESULTS

Out of 2111 CRC patients staged with chest CT, 204 (9.6%) had IPNs and 54/204 (26%) had lung metastases. We identified 4 predictors: "location of primary tumour", "pathological nodal stage", "size of the largest nodule" and "extrapulmonary synchronous metastases at diagnosis". Discrimination of the final model in the validation sample was demonstrated by the difference in mean predicted risk between progressed cases en non-progressed cases (49% versus 21%, p = <0.001).

CONCLUSION

A prediction model with 4 clinical risk factors can be used to assist multidisciplinary teams in the prediction of individualized risk of lung metastases and imaging strategy in patients with IPNs and newly diagnosed colorectal cancer. The model performed well in new patients not included in the model development.

Colorectal Cancer Pulmonary Metastasectomy: When, Why and How

Colorectal cancer is the third-most-diagnosed cancer in males and in females, representing 8% of estimated new cases, and the third cause of cancer-related death in both sexes, accounting for 9% of cancer deaths in men and 8% in women. About 20% of patients diagnosed with CRC present metastatic disease. Although lung metachronous or synchronous metastatic spread without other involved sites has been reported in only a small proportion of patients, considering that this tumor is frequently diagnosed, the clinical approach to CRC pulmonary metastases represents a major issue for thoracic surgeons and CRC oncologists. Among patients diagnosed with pulmonary metastases from CRC, about 9-12% are eligible for local treatments with radical intent, including surgical resection, SBRT (stereotactic body radiation therapy) and ablation therapy. Due to the lack of randomized controlled trials among different local strategies, there is no definitive evidence about the optimal approach, although surgical resection is considered the most effective therapeutic option in this clinical scenario. Oncological achievement of primary radical resection, the biology of primary tumor and metastatic sites, disease free interval and or progression free survival are independent prognostic factors which make it possible to define a cohort of patients which might significantly benefit from pulmonary metastasectomy.

Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation

PURPOSE

To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA).

METHODS

Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis.

RESULTS

A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality.

CONCLUSIONS

The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.

A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.

Lung surveillance following colorectal cancer pulmonary metastasectomy: Utilization of clinicopathologic risk factors to guide strategy

BACKGROUND

Appropriately selected patients clearly benefit from resection of colorectal cancer (CRC) pulmonary metastases (PMs). However, there remains equipoise surrounding optimal chest surveillance strategies following pulmonary metastasectomy. We aimed to identify risk factors that may inform chest surveillance in this population.

METHODS

Patients who underwent CRC pulmonary metastasectomy were identified from a single institution's prospectively maintained surgical database. Clinicopathologic and genomic characteristics were collected. Patients were stratified by diagnosis of subsequent PM within 6 months of the index lung resection. Multivariate modeling was used to evaluate risk factors.

RESULTS

A total of 197 patients met the study's inclusion criteria, of whom 52.3% (n = 103) developed subsequent PM, at a median of 9.51 months following the index metastasectomy. Patients with KRAS alterations (odds ratio [OR], 3.073; 95% confidence interval [CI], 1.363-6.926; P = .007), TP53 alterations (OR, 3.109; 95% CI, 1.318-7.341; P = .010) were found to be at risk of PM diagnosis within 6 months of the index metastasectomy, while those with an APC alteration (OR, .218; 95% CI, 0.080-0.598; P = .003) were protected. Moreover, patients who received systemic therapy within 3 months of the initial PM diagnosis also were more likely to develop early lung recurrence (OR, 2.105; 95% CI, 0.971-4.563; P = .059).

CONCLUSIONS

Patients with KRAS alterations, TP53 alterations, and no APC alterations developed early recurrence in the lung following pulmonary metastasectomy, as did those who received chemotherapy after their initial PM diagnosis. As such, these groups benefit from early lung imaging after metastasectomy, as chest surveillance protocols should be based on patient-centered clinicopathologic and genomic risk factors.

Independent association of the Meckel's cave with trigeminal neuralgia and development of a screening tool

PURPOSE

To 1) investigate the association of the properties of the Meckel's cave (MC) with TN occurrence (i.e., affected vs. unaffected nerves) and whether such association was independent of neurovascular contact (NVC); and 2) develop an objective screening tool for TN.

MATERIALS AND METHODS

Two hundred and nineteen trigeminal nerves were included. (The severity of) NVC was identified for individual nerve, and a set of 107 radiomic features were extracted to characterize various properties of each MC. Both procedures were primarily based on magnetic resonance imaging sequences. A radiomic score (Rad-score) was constructed for each MC to integrate the features associated with TN occurrence. Independent t-test and logistic regression were conducted to assess the association and develop the screening tool mentioned above.

RESULTS

Twelve features were selected to build the Rad-score, with the Inverse Difference Moment Normalized (IDMN) having the greatest weight. The Rad-score was significantly (p ≤ 0.05) higher in the affected compared to the unaffected nerves, irrespective of NVC. The Rad-score and NVC were incorporated in the regression model/screening tool, which demonstrated an acceptable discriminating ability (C-statistic = 0.84).

CONCLUSION

This study has identified a potential association of the properties/features of the MC with TN occurrence, probably involving the demyelination and axonal injury of the trigeminal ganglion within the MC as suggested by the IDMN. Such association may be independent of NVC. This finding may provide new insight into the etiology and/or pathophysiology of TN. The screening tool, which demonstrated an acceptable discriminating ability, may contribute to an improvement in its diagnosis.

Do resected colorectal cancer patients need early chest imaging? Impact of clinicopathologic characteristics on time to development of pulmonary metastases

BACKGROUND AND OBJECTIVES

For patients with colorectal cancer (CRC), the lung is the most common extra-abdominal site of distant metastasis. However, practices for chest imaging after colorectal resection vary widely. We aimed to identify characteristics that may indicate a need for early follow-up imaging.

METHODS

We retrospectively reviewed charts of patients who underwent CRC resection, collecting clinicopathologic details and oncologic outcomes. Patients were grouped by timing of pulmonary metastases (PM) development. Analyses were performed to investigate odds ratio (OR) of PM diagnosis within 3 months of CRC resection.

RESULTS

Of 1600 patients with resected CRC, 233 (14.6%) developed PM, at a median of 15.4 months following CRC resection. Univariable analyses revealed age, receipt of systemic therapy, lymph node ratio (LNR), lymphovascular and perineural invasion, and KRAS mutation as risk factors for PM. Furthermore, multivariable regression showed neoadjuvant therapy (OR: 2.99, p < 0.001), adjuvant therapy (OR: 6.28, p < 0.001), LNR (OR: 28.91, p < 0.001), and KRAS alteration (OR: 5.19, p < 0.001) to predict PM within 3 months post-resection.

CONCLUSIONS

We identified clinicopathologic characteristics that predict development of PM within 3 months after primary CRC resection. Early surveillance in such patients should be emphasized to ensure timely identification and treatment of PM.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

Development and validation of an artificial intelligence prediction model and a survival risk stratification for lung metastasis in colorectal cancer from highly imbalanced data: A multicenter retrospective study

BACKGROUND

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

METHODS

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. In addition, stratified LM patients by risk score were utilized for survival analysis.

RESULTS

Extremely low rates of LM with 2.59% and 4.50% were present in the development and validation set. As the imbalanced learning strategy, the BRF model with an Area under the receiver operating characteristic curve (AUC) of 0.874 and an average precision (AP) of 0.184 performed best compares with other models and clinical predictor. Patients with LM in the high-risk group had significantly poorer survival (P＜0.001) and failed to benefit from resection (P = 0.125).

CONCLUSIONS

In summary, we have utilized the BRF algorithm to develop an effective, non-invasive, and practical model for predicting LM in CRC patients based on highly imbalanced datasets. In addition, we have implemented a novel approach to stratify the survival risk of CRC patients with LM based the output of the model.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma

OBJECTIVES

This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis.

MATERIALS AND METHODS

A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models.

RESULTS

Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models.

CONCLUSIONS

In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.

The prognostic value of COX-2 in predicting metastasis of patients with colorectal cancer: A systematic review and meta analysis

Introduction

COX-2 is overexpressed in colorectal tumour tissue relative to the healthy colonic mucosa, thus we investigated the prognostic significance of COX-2 in determining the metastasis of patients with colorectal cancer.

Methods

PubMed, EMBASE, and Cochrane Library were searched using the following terms colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, Cyclooxygenase-2, and prognosis to identify articles providing information on the prognostic importance of COX-2 in adult patients with metastatic colorectal cancer. Review papers, non-research letters, comments, case reports, animal studies, original research with sample sizes of fewer than 20, case reports and series, non-English language articles, and pediatric studies (those under the age of 17) were excluded. The Newcastle Ottawa Scale (NOS) was used to assess the credibility of the included studies. The full texts were evaluated and this study complied with the terms of the local protocol and the Helsinki Declaration.

Results

Eight relevant studies were included in this review involving 937 patients. The meta-analysis revealed that COX-2 expression is associated with lymph node invasion (RR 1.85 [1.21, 2.83], P = 0.005, I2 = 88 %) and liver metastasis (RR 4.90 [1.12, 21.57], P = 0.04, I2 = 42 %), but not with venous dissemination (RR 1.48 [0.72, 3.03], P = 0.28, I2 = 87 %).

Conclusion

COX-2 expression is associated with lymph node invasion in colorectal cancer but further studies are required to determine the prognostic significance of COX-2 expression in determining metastasis status for colorectal cancer patients.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors

OBJECTIVES

This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT).

METHODS

Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis.

RESULTS

The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model.

CONCLUSIONS

The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning.

KEY POINTS

• A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Identification of high-risk factors for recurrence of colon cancer following complete mesocolic excision: An 8-year retrospective study

BACKGROUND

Colon cancer recurrence is a common adverse outcome for patients after complete mesocolic excision (CME) and greatly affects the near-term and long-term prognosis of patients. This study aimed to develop a machine learning model that can identify high-risk factors before, during, and after surgery, and predict the occurrence of postoperative colon cancer recurrence.

METHODS

The study included 1187 patients with colon cancer, including 110 patients who had recurrent colon cancer. The researchers collected 44 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination information, type of surgery, and intraoperative information. Four machine learning algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. The researchers evaluated the model using the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA), and external validation.

RESULTS

Among the four prediction models, the XGBoost algorithm performed the best. The ROC curve results showed that the AUC value of XGBoost was 0.962 in the training set and 0.952 in the validation set, indicating high prediction accuracy. The XGBoost model was stable during internal validation using the k-fold cross-validation method. The calibration curve demonstrated high predictive ability of the XGBoost model. The DCA curve showed that patients who received interventional treatment had a higher benefit rate under the XGBoost model. The external validation set's AUC value was 0.91, indicating good extrapolation of the XGBoost prediction model.

CONCLUSION

The XGBoost machine learning algorithm-based prediction model for colon cancer recurrence has high prediction accuracy and clinical utility.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

A prediction model of cognitive impairment risk in elderly illiterate Chinese women

Objective

To establish and validate a targeted model for the prediction of cognitive impairment in elderly illiterate Chinese women.

Methods

1864 participants in the 2011-2014 cohort and 1,060 participants in the 2014-2018 cohort from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to measure cognitive function. Demographics and lifestyle information were collected to construct a risk prediction model by a restricted cubic spline Cox regression. The discrimination and accuracy of the model were assessed by the area under the curve (AUC) and the concordance index, respectively.

Results

A total of seven critical variables were included in the final prediction model for cognitive impairment risk, including age, MMSE score, waist-to-height ratio (WHtR), psychological score, activities of daily living (ADL), instrumental abilities of daily living (IADL), and frequency of tooth brushing. The internal and external validation AUCs were 0.8 and 0.74, respectively; and the receiver operating characteristic (ROC) curves indicated good performance ability of the constructed model.

Conclusion

A feasible model to explore the factors influencing cognitive impairment in elderly illiterate women in China and to identify the elders at high risk was successfully constructed.

Development and Validation of a Prognostic Model for Overall Survival in Patients with Primary Pelvis and Spine Osteosarcoma: A Population-Based Study and External Validation

Background: Primary pelvis and spine osteosarcoma (PSOS) is a specific type of osteosarcoma that is difficult to treat and has a poor prognosis. In recent years, the research on osteosarcoma has been increasing, but there have been few studies on PSOS; in particular, there have been a lack of analyses with a large sample size. This study aimed to construct and validate a model to predict the overall survival (OS) of PSOS patients, as currently there are no tools available for assessing their prognosis. Methods: Data including demographic information, clinical characteristics, and follow-up information on patients with PSOS were collected from the Surveillance, Epidemiology, and End Results (SEER) database, as well as from the Spine Tumor Center of Changzheng Hospital. Variable selection was achieved through a backward procedure based on the Akaike Information Criterion (AIC). Prognostic factors were identified by univariate and multivariate Cox analysis. A nomogram was further constructed for the estimation of 1-, 3-, and 5-year OS. Calibration plots, the concordance index (C-index), and the receiver operating characteristic (ROC) were used to evaluate the prediction model. Results: In total, 83 PSOS patients and 90 PSOS patients were separately collected from the SEER database and Changzheng Hospital. In the SEER cohort, liver metastasis, lung metastasis, and chemotherapy were recognized as independent prognostic factors for OS (p < 0.05) and were incorporated to construct the initial nomogram. However, the initial nomogram showed poor predictive accuracy in internal and external validation. Then, we shifted our focus to the Changzheng data. Lung metastasis involving segments, Eastern Cooperative Oncology Group (ECOG) performance score, alkaline phosphatase (ALP) level, and en bloc resection were ultimately identified as independent prognostic factors for OS (p < 0.05) and were further incorporated to construct the current nomogram, of which the bias-corrected C-index was 0.834 (0.824–0.856). The areas under the ROC curves (AUCs) of the current nomogram regarding 1-, 3-, and 5-year OS probabilities were 0.93, 0.96, and 0.92, respectively. Conclusion: We have developed a predictive model with satisfactory performance and clinical practicability, enabling effective prediction of the OS of PSOS patients and aiding clinicians in decision-making.

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Purpose

This study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.

Materials and methods

There were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P &lt; 0.05 was considered significant.

Result

Tumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.

Data conclusion

A nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

Radiomics in Lung Metastases: A Systematic Review

Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms

OBJECTIVE

This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms.

METHODS

This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters.

RESULTS

Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished.

CONCLUSIONS

Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.

Crafting a prognostic nomogram for the overall survival rate of cutaneous verrucous carcinoma using the surveillance, epidemiology, and end results database

BACKGROUND

The aim of this study was to establish and verify a predictive nomogram for patients with cutaneous verrucous carcinoma (CVC) who will eventually survive and to determine the accuracy of the nomogram relative to the conventional American Joint Committee on Cancer (AJCC) staging system.

METHODS

Assessments were performed on 1125 patients with CVC between 2004 and 2015, and the results of those examinations were recorded in the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided at a ratio of 7:3 into the training (n = 787) and validation (n = 338) cohorts. Predictors were identified using stepwise regression analysis in the COX regression model for create a nomogram to predict overall survival of CVC patients at 3-, 5-, and 8-years post-diagnosis. We compared the performance of our model with that of the AJCC prognosis model using several evaluation metrics, including C-index, NRI, IDI, AUC, calibration plots, and DCAs.

RESULTS

Multivariate risk factors including sex, age at diagnosis, marital status, AJCC stage, radiation status, and surgery status were employed to determine the overall survival (OS) rate (P<0.05). The C-index nomogram performed better than the AJCC staging system variable for both the training (0.737 versus 0.582) and validation cohorts (0.735 versus 0.573), which AUC (> 0.7) revealed that the nomogram exhibited significant discriminative ability. The statistically significant NRI and IDI values at 3-, 5-, and 8-year predictions for overall survival (OS) in the validation cohort (55.72%, 63.71%, and 78.23%, respectively and 13.65%, 20.52%, and 23.73%, respectively) demonstrate that the established nomogram outperforms the AJCC staging system (P < 0.01) in predicting OS for patients with cutaneous verrucous carcinoma (CVC). The calibration plots indicate good performance of the nomogram, while decision curve analyses (DCAs) show that the predictive model could have a favorable clinical impact.

CONCLUSION

This study constructed and validated a nomogram for predicting the prognosis of patients with CVC in the SEER database and assessed it using several variables. This nomogram model can assist clinical staff in making more-accurate predictions than the AJCC staging method about the 3-, 5-, and 8-year OS probabilities of patients with CVC.

Comparison of nomogram with random survival forest for prediction of survival in patients with spindle cell carcinoma

Purpose

Spindle cell carcinoma (SpCC) is a relatively rare tumor with an unfavorable prognosis. This study aimed to develop and validate a prediction model for the individual survival of patients with SpCC using Cox regression and the random survival forest (RSF) model.

Methods

Patients diagnosed with SpCC between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into training and validating cohorts. Cox regression and RSF were used to identify prognostic predictors and build prediction models. A nomogram based on Cox regression was constructed to predict the 1-, 3-, and 5-year survival of patients with SpCC. Internal validation was conducted using the bootstrapping method. We evaluated the discrimination accuracy and calibration of the model using Harrell's C-index and calibration plot, respectively.

Results

Two hundred and fifty patients diagnosed with SpCC with required information were enrolled in this study. Multivariate Cox regression and RSF identified age, primary site, grade, SEER stage, tumor size, and treatment as significant prognostic predictors of SpCC. The bootstrapped and validated C-indices were 0.812 and 0.783 for nomogram, and 0.790 and 0.768 for RSF, respectively. Calibration plot of the nomogram showed an agreement between the prediction and actual observation.

Conclusions

The nomogram developed in this study is a promising tool with a simplified presentation that can easily be used and interpreted by clinicians for evaluating the survival of each patient with SpCC; its performance was comparable to that of RSF. Application of such models are needed to help oncologists identify the high-risk patients and improve clinical decision making of SpCC treatment.

A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients

PURPOSE

This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN).

MATERIALS AND METHODS

In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM.

RESULTS

The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features.

CONCLUSION

Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.

Diagnostic models for fever of unknown origin based on 18F-FDG PET/CT: a prospective study in China

Background

This study aims to analyze the 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) characteristics of different causes of fever of unknown origin (FUO) and identify independent predictors to develop a suitable diagnostic model for distinguishing between these causes. A total of 524 patients with classical FUO who underwent standard diagnostic procedures and PET/CT were prospectively studied. The diagnostic performance of PET/CT imaging was analyzed, and relevant clinical parameters that could improve diagnostic efficacy were identified. The model was established using the data of 369 patients and the other 155 patients comprised the validation cohort for verifying the diagnostic performance of the model.

Results

The metabolic characteristics of the “hottest” lesion, the spleen, bone marrow, and lymph nodes varied for various causes. PET/CT combined with clinical parameters achieved better discrimination in the differential diagnosis of FUO. The etiological diagnostic models included the following factors: multisite metabolic characteristics, blood cell counts, inflammatory indicators (erythrocyte sedimentation rate, C-reactive protein, serum ferritin, and lactate dehydrogenase), immunological indicators (interferon gamma release assay, antinuclear antibody, and anti-neutrophil cytoplasm antibody), specific signs (weight loss, rash, and splenomegaly), and age. In the testing cohort, the AUCs of the infection prediction model, the malignancy diagnostic model, and the noninfectious inflammatory disease prediction model were 0.89 (95% CI 0.86–0.92), 0.94 (95% CI 0.92–0.97), and 0.95 (95% CI 0.93–0.97), respectively. The corresponding AUCs for the validation cohort were 0.88 (95% CI 0.82–0.93), 0.93 (95% CI 0.89–0.98), and 0.95 (95% CI 0.92–0.99), respectively.

Conclusions

18F-FDG PET/CT has a certain level of sensitivity and accuracy in diagnosing FUO, which can be further improved by combining it with clinical parameters. Diagnostic models based on PET/CT show excellent performance and can be used as reliable tools to discriminate the cause of FUO.

Trial registration This study (a two-step method apparently improved the physicians’ level of diagnosis decision-making for adult patients with FUO) was registered on the website http://www.clinical-trials.gov on January 14, 2014, with registration number NCT02035670.

Development of survival predictors for high-grade serous ovarian cancer based on stable radiomic features from computed tomography images

Less than 35% of advanced patients with high-grade serous ovarian cancer (HGSOC) survive for 5 years after diagnosis. Here, we developed radiomics-based models to predict HGSOC clinical outcomes using preoperative contrast-enhanced computed tomography (CECT) images. 891 radiomics features were extracted between primary, metastatic, or lymphatic lesions from preoperative venous phase CECT images of 217 patients with HGSOC. A heuristic method, Frequency Appearance in Multiple Univariate preScreening (FAMUS), was proposed to identify stable and task-relevant radiomic features. Using FAMUS, we constructed predictive models of overall survival and disease-free survival in patients with HGSOC based on these stable radiomic features. According to their CT images, patients with HGSOC can be accurately stratified into high-risk or low-risk groups for cancer-related death within 2-6 years or for likely recurrence within 1-5 years. These radiomic models provide convincing and reliable non-invasive markers for individualized prognostic evaluation and clinical decision-making for patients with HGSOC.

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Frequency Appearance in Multiple Univariate preScreening (FAMUS) identifies stable and task-relevant radiomic features from computed tomography (CT) images

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Radiomics-based signatures are highly predictive of the clinical outcome of high-grade serous ovarian cancer (HGSOC)

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FAMUS improves the prognostic performance of radiomics-based prediction models

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Developed radiomic models can help clinicians tailor treatment plans for HGSOC

A web-based prognostic nomogram for the cancer specific survival of elderly patients with T1-T3N0M0 renal pelvic transitional cell carcinoma based on the surveillance, epidemiology, and end results database

Background

At present, there are few studies on renal pelvic transitional cell carcinoma (RPTCC) in elderly patients in the literature. The study aims to establish a new nomogram of cancer-specific survival (CSS) in elderly patients with T1-T3N0M0 RPTCC and validate its reliability.

Methods

This study downloaded the data of 1375 elderly patients with T1-T3N0M0 RPTCC in the Surveillance, Epidemiology, and Final Results (SEER) database from 2004 to 2018. Patients were randomly divided into training cohort (n = 977) and validation cohort (n = 398). Proportional subdistribution hazard analyse was applied to determine independent prognostic factors. Based on these factors, we constructed a compting risk model nomogram. We use the calibration plots, the area under the receiver operating characteristics curve (AUC), concordance index (C-index), and decision curve analysis (DCA) to validate predictive performance and clinical applicability. Patients were divided into low-risk group and high-risk group based on nomogram risk score. Kaplan–Meier curve was applied to analyze the difference in survival curve between the two groups of patients.

Results

We found that the risk factors affecting CSS in elderly patients with T1-T3N0M0 RPTCC are surgery, AJCC stage, laterality, tumor size, histological grade, and tumour laterality. Based on these factors, we established a nomogram to predict the CSS of RPTCC patients at 1-, 3-, and 5-year. The calibration plots showed that the predicted value was highly consistent with the observed value. In the training cohort and validation cohort, the C-index of the nomogram were 0.671(95% CI 0.622–0.72) and 0.679(95% CI 0.608–0.750), respectively, the AUC showed similar results. The DCA suggests that namogram performs better than the AJCC stage system. The Kaplan–Meier curve showed that CSS of patients was significantly higher in the low-risk group.

Conclusions

In this study, the SEER database was used for the first time to create and validate a new nomogram prediction model for elderly patients with T1-T3N0M0 RPTCC. Compared with the traditional AJCC stage system, our new nomogram can more accurately predict the CSS of elderly patients with T1-T3N0M0 RPTCC, which is helpful for patient prognosis assessment and treatment strategies selection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01028-1.

A Nomogram for Predicting Cancer-Specific Survival of Osteosarcoma and Ewing's Sarcoma in Children: A SEER Database Analysis

Background

Osteosarcoma (OSC) and Ewing's sarcoma (EWS) are children's most common primary bone tumors. The purpose of the study is to develop and validate a new nomogram to predict the cancer-specific survival (CSS) of childhood OSC and EWS.

Methods

The clinicopathological information of all children with OSC and EWS from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen children's independent risk factors for CSS. These risk factors were used to construct a nomogram to predict the CSS of children with OSC and EWS. A series of validation methods, including calibration plots, consistency index (C-index), and area under the receiver operating characteristic curve (AUC), were used to validate the accuracy and reliability of the prediction model. Decision curve analysis (DCA) was used to validate the clinical application efficacy of predictive models. All patients were divided into low- and high-risk groups based on the nomogram score. Kaplan-Meier curve and log-rank test were used to compare survival differences between the two groups.

Results

A total of 2059 children with OSC and EWS were included. All patients were randomly divided into training cohort 60% (N = 1215) and validation cohort 40% (N = 844). Univariate and multivariate analysis suggested that age, surgery, stage, primary site, tumor size, and histological type were independent risk factors. Nomograms were established based on these factors to predict 3-, 5-, and 8-years CSS of children with OSC and EWS. The calibration plots showed that the predicted value was highly consistent with the actual value. In the training cohort and validation cohort, the C-index was 0.729 (0.702–0.756) and 0.735 (0.702–0.768), respectively. The AUC of the training cohort and the validation cohort also showed similar results. The DCA showed that the nomogram had good clinical value.

Conclusion

We constructed a new nomogram to predict the CSS of OSC and EWS in children. This predictive model has good accuracy and reliability and can help doctors and patients develop clinical strategies.

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing CT radiomics and deep-learning features of primary lesions and peripheral lymph nodes

Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In this study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76±0.08 and 0.91±0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine. This article is protected by copyright. All rights reserved.

Deep learning prediction of axillary lymph node status using ultrasound images

OBJECTIVE

To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images.

METHODS

In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not).

RESULTS

Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS).

CONCLUSION

It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer.

A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study

Objective

Thermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy.

Methods

This study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making.

Results

Thirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful.

Conclusion

A CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.

Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning

Introduction:

Patients with hemorrhagic transformation (HT) were reported to have hemorrhage expansion. However, identification these patients with high risk of hemorrhage expansion has not been well studied.

Objectives:

We aimed to develop a radiomic score to predict hemorrhage expansion after HT among patients treated with thrombolysis/thrombectomy during acute phase of ischemic stroke.

Methods:

A total of 104 patients with HT after reperfusion treatment from the West China hospital, Sichuan University, were retrospectively included in this study between 1 January 2012 and 31 December 2020. The preprocessed initial non-contrast-enhanced computed tomography (NECT) imaging brain images were used for radiomic feature extraction. A synthetic minority oversampling technique (SMOTE) was applied to the original data set. The after-SMOTE data set was randomly split into training and testing cohorts with an 8:2 ratio by a stratified random sampling method. The least absolute shrinkage and selection operator (LASSO) regression were applied to identify candidate radiomic features and construct the radiomic score. The performance of the score was evaluated by receiver operating characteristic (ROC) analysis and a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model.

Results:

Among the 104 patients, 17 patients were identified with hemorrhage expansion after HT detection. A total of 154 candidate predictors were extracted from NECT images and five optimal features were ultimately included in the development of the radiomic score by using logistic regression machine-learning approach. The radiomic score showed good performance with high area under the curves in both the training data set (0.91, sensitivity: 0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60; specificity: 0.85), and original data set (0.82, sensitivity: 0.77; specificity: 0.78). The calibration curve and DCA also indicated that there was a high accuracy and clinical usefulness of the radiomic score for hemorrhage expansion prediction after HT.

Conclusions:

The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.

A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study

Background

The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC.

Methods

The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA).

Results

The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage.

Conclusion

A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.

Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models

PURPOSE

To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase.

METHODS

CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models.

RESULTS

Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively.

CONCLUSION

This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.

Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging

Background

Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs.

Methods

The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group.

Results

The optimal features (“GLCMEntropy_angle135_offset1” and “Sphericity”) were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability.

Conclusion

The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.

Recurrence Risk of Liver Cancer Post-hepatectomy Using Machine Learning and Study of Correlation With Immune Infiltration

Postoperative recurrence of liver cancer is the main obstacle to improving the survival rate of patients with liver cancer. We established an mRNA-based model to predict the risk of recurrence after hepatectomy for liver cancer and explored the relationship between immune infiltration and the risk of recurrence after hepatectomy for liver cancer. We performed a series of bioinformatics analyses on the gene expression profiles of patients with liver cancer, and selected 18 mRNAs as biomarkers for predicting the risk of recurrence of liver cancer using a machine learning method. At the same time, we evaluated the immune infiltration of the samples and conducted a joint analysis of the recurrence risk of liver cancer and found that B cell, B cell naive, T cell CD4⁺ memory resting, and T cell CD4⁺ were significantly correlated with the risk of postoperative recurrence of liver cancer. These results are helpful for early detection, intervention, and the individualized treatment of patients with liver cancer after surgical resection, and help to reveal the potential mechanism of liver cancer recurrence.

The use of radiomic analysis of magnetic resonance imaging in predicting distant metastases of rectal carcinoma following surgical resection: A systematic review and meta-analysis

AIM

Estimating prognosis in rectal carcinoma (RC) is challenging, with distant recurrence (DR) occurring in up to 30% of cases. Radiomics is a novel field using diagnostic imaging to investigate the tumour heterogeneity of cancers and may have the potential to predict DR. The aim of the study was to perform a systematic review of the current literature evaluating the use of radiomics in predicting DR in patients with resected RC.

METHODS

A systematic review was performed as per PRISMA guidelines to identify studies reporting radiomic analysis of magnetic resonance imaging (MRI) to predict DR in patients diagnosed with RC. Sensitivity and specificity of radiomic analyses were included for meta-analysis.

RESULTS

A total of seven studies including 1497 patients (998 males) were included, seven, five and one of whom reported radiomics, respectively. The overall pooled rate of DR from all included studies was 17.1% (256/1497), with 15.6% (236/1497), 1.3% (19/1497) and 0.2% (3/1497) of patients having hepatic, pulmonary and peritoneal metastases. Meta-analysis demonstrated that radiomics correctly predicted DR with pooled sensitivities and specificities of MRI 0.76 (95% CI: 0.73, 0.78) and 0.85 (95% CI: 0.83, 0.88), respectively.

CONCLUSION

This systematic review suggests the benefit of radiomic analysis of preoperative MRI in identifying patients with resected RC at an increased risk of DR. Our findings warrant validation in larger prospective studies as modalities to predict DR is a significant unmet need in RC. Radiomics may allow for tailored therapeutic strategies for high-risk groups.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer

BACKGROUND

Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method.

AIM

To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC.

METHODS

A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging.

RESULTS

The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858).

CONCLUSION

MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.

An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients

Objectives

To identify the relatively invariable radiomics features as essential characteristics during the growth process of metastatic pulmonary nodules with a diameter of 1 cm or smaller from colorectal cancer (CRC).

Methods

Three hundred and twenty lung nodules were enrolled in this study (200 CRC metastatic nodules in the training cohort, 60 benign nodules in the verification cohort 1, 60 CRC metastatic nodules in the verification cohort 2). All the nodules were divided into four groups according to the maximum diameter: 0 to 0.25 cm, 0.26 to 0.50 cm, 0.51 to 0.75 cm, 0.76 to 1.0 cm. These pulmonary nodules were manually outlined in computed tomography (CT) images with ITK-SNAP software, and 1724 radiomics features were extracted. Kruskal-Wallis test was performed to compare the four different levels of nodules. Cross-validation was used to verify the results. The Spearman rank correlation coefficient is calculated to evaluate the correlation between features.

Results

In training cohort, 90 features remained stable during the growth process of metastasis nodules. In verification cohort 1, 293 features remained stable during the growth process of benign nodules. In verification cohort 2, 118 features remained stable during the growth process of metastasis nodules. It is concluded that 20 features remained stable in metastatic nodules (training cohort and verification cohort 2) but not stable in benign nodules (verification cohort 1). Through the cross-validation (n=100), 11 features remained stable more than 90 times.

Conclusions

This study suggests that a small number of radiomics features from CRC metastatic pulmonary nodules remain relatively stable from small to large, and they do not remain stable in benign nodules. These stable features may reflect the essential characteristics of metastatic nodules and become a valuable point for identifying metastatic pulmonary nodules from benign nodules.

Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study

OBJECTIVES

To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context.

MATERIALS AND METHODS

This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection.

RESULTS

The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17).

CONCLUSIONS

Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients.

KEY POINTS

• Adjuvant therapy decision in colorectal cancer can be a challenge in medical oncology. • Radiomics models, derived from diagnostic CT, trained and validated in a two-center cohort, could predict recurrence in stage II and III colorectal cancer patients. • Identifying patients with a low risk of recurrence, these models could facilitate treatment optimization and avoid unnecessary treatment.

Dealing with indeterminate pulmonary nodules in colorectal cancer patients; a systematic review

INTRODUCTION

Indeterminate pulmonary nodules (IPNs) are frequently encountered on staging computed tomography (CT) in colorectal cancer (CRC) patients and they create diagnostic dilemmas. This systematic review and pooled analysis aims to estimate the incidence and risk of malignancy of IPNs and provide an overview of the existing literature on IPNs in CRC patients.

MATERIALS AND METHODS

EMBASE, Pubmed and the Cochrane database were searched for papers published between January 2005 and April 2020. Studies describing the incidence of IPNs and the risk of malignancy in CRC patients and where the full text was available in the English language were considered for inclusion. Exclusion criteria included studies that used chest X-ray instead of CT, liver metastasis cohorts, studies with less than 60 CRC patients and reviews.

RESULTS

A total of 18 studies met the inclusion criteria, involving 8637 patients. Pooled analysis revealed IPNs on staging chest CT in 1327 (15%) of the CRC patients. IPNs appeared to be metastatic disease during follow up in 16% of these patients. Regional lymph node metastases, liver metastases, location of the primary tumour in the rectum, larger IPN size and multiple IPNs are the five most frequently reported parameters predicting the risk of malignancy of IPNs.

CONCLUSION

A risk stratification model for CRC patients with IPNs is warranted to enable an adequate selection of high risk patients for IPN follow up and to diminish the use of unnecessary repetitive chest CT-scans in the many low risk patients.

Radiomics and radiogenomics in ovarian cancer: a literature review

Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.

Development and validation of a novel prognostic model for long-term overall survival in liposarcoma patients: a population-based study

Objective

To construct and validate a clinically accurate and histology-specific nomogram to predict overall survival (OS) among liposarcoma (LPS) patients.

Methods

We retrospectively screened eligible patients with LPS diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database. We screened independent predictors for the nomogram using univariate and multivariate analyses. We then evaluated the prognostic accuracy of the nomogram by receiver operating characteristic (ROC) curve analysis and Harrell’s concordance index. The prognostic performances of the nomogram and the American Joint Committee on Cancer (AJCC) seventh edition staging system were compared using integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analyses (DCA).

Results

A novel nomogram was developed using independent prognostic variables, which exhibited excellent predictive performances for 3- and 5-year OS according to ROC curves. The C-index proved that the proposed nomogram had better prognostic accuracy for LPS than the traditional AJCC system, while the NRI, IDI, and DCA of the nomogram indicated better clinical net benefit.

Conclusions

The proposed nomogram can predict 3- and 5-year OS of LPS patients with reliable accuracy and may thus help clinicians to develop appropriate clinical therapies and counseling strategies to prolong the life expectancy of these patients.

Clinical analysis and a novel risk predictive nomogram for 155 adult patients with hemophagocytic lymphohistiocytosis

Recently, more and more attention has been paid on adult hemophagocytic lymphohistiocytosis (HLH), a disease with complicated symptoms and high mortality. In order to analyze the clinical characteristics and prediction risk factors of mortality, we designed a retrospective study with 1-year follow-up and included 155 patients admitted to Tongji Hospital diagnosed as HLH. One hundred seven patients formed the training cohort for nomogram development, and 48 patients formed the validation cohort to confirm the model's performance. All patients' clinical characteristics, laboratory results, medical records, and prognosis were analyzed. Among all the 107 patients in the training cohort, 46 were male and 61 were female, with the median age of 49.0 (IQR 31.0-63.0). The 1-year mortality rate was 43.9% (47/107) and 45.8% (22/48) in the training and validation cohort, respectively. And further multivariate logistic regression analysis in the training cohort showed that male (odds ratio 5.534, 95% CI 1.507-20.318, p = 0.010), altered mental status (11.876, 1.882-74.947, p = 0.008), serum ferritin ≥ 31,381 μg/L (8.273, 1.855-36.883, p = 0.006), and IL-6 ≥ 18.59 pg/mL (19.446, 1.527-247.642, p = 0.022) were independent risk factor of mortality. A nomogram included the four prediction factors mentioned above was also tabled to help clinicians evaluate the probability of poor outcome. Area under the receiver operating characteristic curve (AUROC) analysis, calibration curves, and decision curve analysis (DCA) certify the accuracy and the clinical usefulness of the nomogram. Our research reveals that male, altered mental status, serum ferritin ≥ 31,381 µg/L, and IL-6 ≥ 18.59 pg/mL are four independent predictors for poor prognosis. Doctors should pay more attention to patients with altered mental status, high serum ferritin, and IL-6 level, who have a higher risk of death.

Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone

PURPOSE

To assess whether CT-based radiomics of the ablation zone (AZ) can predict local tumour progression (LTP) after thermal ablation for colorectal liver metastases (CRLM).

MATERIALS AND METHODS

Eighty-two patients with 127 CRLM were included. Radiomics features (with different filters) were extracted from the AZ and a 10 mm periablational rim (PAR)on portal-venous-phase CT up to 8 weeks after ablation. Multivariable stepwise Cox regression analyses were used to predict LTP based on clinical and radiomics features. Performance (concordance [c]-statistics) of the different models was compared and performance in an 'independent' dataset was approximated with bootstrapped leave-one-out-cross-validation (LOOCV).

RESULTS

Thirty-three lesions (26 %) developed LTP. Median follow-up was 21 months (range 6-115). The combined model, a combination of clinical and radiomics features, included chemotherapy (HR 0.50, p = 0.024), cT-stage (HR 10.13, p = 0.016), lesion size (HR 1.11, p = <0.001), AZ_Skewness (HR 1.58, p = 0.016), AZ_Uniformity (HR 0.45, p = 0.002), PAR_Mean (HR 0.52, p = 0.008), PAR_Skewness (HR 1.67, p = 0.019) and PAR_Uniformity (HR 3.35, p < 0.001) as relevant predictors for LTP. The predictive performance of the combined model (after LOOCV) yielded a c-statistic of 0.78 (95 %CI 0.65-0.87), compared to the clinical or radiomics models only (c-statistic 0.74 (95 %CI 0.58-0.84) and 0.65 (95 %CI 0.52-0.83), respectively).

CONCLUSION

Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. CT-based radiomics of the AZ and PAR may have potential to aid in the prediction of LTP during follow-up in patients with CRLM.

Radiomics complements clinical, radiological, and technical features to assess local control of colorectal cancer lung metastases treated with radiofrequency ablation

OBJECTIVES

Radiofrequency ablation (RFA) of lung metastases of colorectal origin can improve patient survival and quality of life. Our aim was to identify pre- and per-RFA features predicting local control of lung metastases following RFA.

METHODS

This case-control single-center retrospective study included 119 lung metastases treated with RFA in 48 patients (median age: 60 years). Clinical, technical, and radiological data before and on early CT scan (at 48 h) were retrieved. After CT scan preprocessing, 64 radiomics features were extracted from pre-RFA and early control CT scans. Log-rank tests were used to detect categorical variables correlating with post-RFA local tumor progression-free survival (LTPFS). Radiomics prognostic scores (RPS) were developed on reproducible radiomics features using Monte-Carlo cross-validated LASSO Cox regressions.

RESULTS

Twenty-six of 119 (21.8%) nodules demonstrated local progression (median delay: 11.2 months). In univariate analysis, four non-radiomics variables correlated with post-RFA-LTPFS: nodule size (> 15 mm, p < 0.001), chosen electrode (with difference between covered array and nodule diameter < 20 mm or non-expandable electrode, p = 0.03), per-RFA intra-alveolar hemorrhage (IAH, p = 0.002), and nodule location into the ablation zone (not seen or in contact with borders, p = 0.005). The highest prognostic performance was reached with the multivariate model including a RPS built on 4 radiomics features from pre-RFA and early revaluation CT scans (cross-validated concordance index= 0.74) in which this RPS remained an independent predictor (cross-validated HR = 3.49, 95% confidence interval = [1.76 - 6.96]).

CONCLUSIONS

Technical, radiological, and radiomics features of the lung metastases before RFA and of the ablation zone at 48 h can help discriminate nodules at risk of local progression that could benefit from complementary local procedure.

KEY POINTS

• The highest prognostic performance to predict post-RFA LTPFS was reached with a parsimonious model including a radiomics score built with 4 radiomics features. • Nodule size, difference between electrode diameter, use of non-expandable electrode, per-RFA hemorrhage, and a tumor not seen or in contact with the ablation zone borders at 48-h CT were correlated with post-RFA LTPFS.

A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland

OBJECTIVES

Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland.

METHODS

A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models.

RESULTS

Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness.

CONCLUSIONS

The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process.

KEY POINTS

• Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.

Individualized Prediction of Colorectal Cancer Metastasis Using a Radiogenomics Approach

Objectives: To evaluate whether incorporating the radiomics, genomics, and clinical features allows prediction of metastasis in colorectal cancer (CRC) and to develop a preoperative nomogram for predicting metastasis.

Methods: We retrospectively analyzed radiomics features of computed tomography (CT) images in 134 patients (62 in the primary cohort, 28 in the validation cohort, and 44 in the independent-test cohort) clinicopathologically diagnosed with CRC at Dazhou Central Hospital from February 2018 to October 2019. Tumor tissues were collected from all patients for RNA sequencing, and clinical data were obtained from medical records. A total of 854 radiomics features were extracted from enhanced venous-phase CT of CRC. Least absolute shrinkage and selection operator regression analysis was utilized for data dimension reduction, feature screen, and radiomics signature development. Multivariable logistic regression analysis was performed to build a multiscale predicting model incorporating the radiomics, genomics, and clinical features. The receiver operating characteristic curve, calibration curve, and decision curve were conducted to evaluate the performance of the nomogram.

Results: The radiomics signature based on 16 selected radiomics features showed good performance in metastasis assessment in both primary [area under the curve (AUC) = 0.945, 95% confidence interval (CI) 0.892–0.998] and validation cohorts (AUC = 0.754, 95% CI 0.570–0.938). The multiscale nomogram model contained radiomics features signatures, four-gene expression related to cell cycle pathway, and CA 19-9 level. The multiscale model showed good discrimination performance in the primary cohort (AUC = 0.981, 95% CI 0.953–1.000), the validation cohort (AUC = 0.822, 95% CI 0.635–1.000), and the independent-test cohort (AUC = 0.752, 95% CI 0.608–0.896) and good calibration. Decision curve analysis confirmed the clinical application value of the multiscale model.

Conclusion: This study presented a multiscale model that incorporated the radiological eigenvalues, genomics features, and CA 19-9, which could be conveniently utilized to facilitate the individualized preoperatively assessing metastasis in CRC patients.

Development of a dual-energy spectral computed tomography-based nomogram for the preoperative discrimination of histological grade in colorectal adenocarcinoma patients

Background

The usefulness of a dual-energy spectral computed tomography (DESCT)-based nomogram in discriminating between histological grades of colorectal adenocarcinoma (CRAC) is unclear. This study aimed to develop such a nomogram and assess its ability to preoperatively discriminate between histological grades in CRAC patients.

Methods

Primary tumors monochromatic CT value, iodine concentration (IC) value, and effective atomic number (Eff-Z) in the arterial (AP) and venous phases (VP) were retrospectively compared between patients with high-grade (n=65) and low-grade (n=108) CRAC who underwent preoperative abdominal DESCT. Univariate analysis was used to compare the DESCT parameters and clinical factors between these two patient groups. Statistically significant features in the univariate analysis were included in the multivariate logistic regression model to identify the indicators for building a nomogram that could discriminate between histological grades in CRAC patients. The clinical usefulness of the nomogram and its value for predicting overall survival were statistically evaluated.

Results

The logistic regression analysis showed that age, clinical T stage, clinical N stage, and IC values in AP and VP were significant independent predictors for high-grade CRAC. A quantitative nomogram developed based on these predictors showed excellent performance for discriminating between the histological grades, with an area under the curve (AUC) of 0.886 and excellent agreement in the calibration curve. The Kaplan-Meier curve for overall survival showed that our nomogram identified a significant difference between the high- and low-risk groups [hazard ratio (HR), 2.188; 95% CI, 1.072-4.465; P=0.027).

Conclusions

This study presents a nomogram that incorporates DESCT parameters and clinical factors and can potentially be used as a clinical tool for individual preoperative prediction of CRAC histological grade.