MRI Predictors of Malignant Transformation in Patients with Inverted Papilloma: A Decision Tree Analysis Using Conventional Imaging Features and Histogram Analysis of Apparent Diffusion Coefficients

[Inverted papilloma of the nose and paranasal sinuses : Diagnosis, treatment, and malignant transformation]

Inverted papilloma (IP) are benign tumors that show a locally aggressive behavior, a high rate of recurrence, and a potential for malignant transformation. Specific radiological signs such as hyperostosis at the origin of the IP and convoluted cerebriform patterns, as well as the typical endoscopic aspect, can lead to diagnosis and enable preoperative planning of surgical access and the extent of surgery. Endonasal endoscopic techniques are considered the gold standard and the introduction of extended surgical techniques such as the prelacrimal approach, frontal drillout, or orbital transposition facilitate complete subperiosteal resection with preservation of important physiological structures. There is a risk of synchronous and metachronous squamous cell carcinomas (IP-SCC). Research focuses on radiological criteria to differentiate benign IP from IP-SCC, genetic and epigenetic factors in the process of malignant transformation, and estimation of the risk of IP progressing to IP-SCC.

Multiparametric MRI-based radiomics nomogram for predicting malignant transformation of sinonasal inverted papilloma

AIM

To investigate the feasibility of a radiomics nomogram model for predicting malignant transformation in sinonasal inverted papilloma (IP) based on radiomic signature and clinical risk factors.

MATERIALS AND METHODS

This single institutional retrospective review included a total of 143 patients with IP and 75 patients with IP with malignant transformation to squamous cell carcinoma (IP-SCC). All patients underwent surgical pathology and had preoperative magnetic resonance imaging (MRI) and computed tomography (CT) sinus studies between June 2014 and February 2022. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI), T2-weighted images (T2WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) were performed to select the features extracted from the sequences mentioned above. Independent clinical risk factors were identified by multivariate logistic regression analysis. Radiomics nomogram was constructed by incorporating independent clinical risk factors and radiomics signature. Based on discrimination and calibration, the diagnostic performance of the nomogram was evaluated.

RESULTS

Twelve radiomics features were selected to develop the radiomics model with an area under the curve (AUC) of 0.987 and 0.989, respectively. Epistaxis (p=0.011), T2 equal signal (p=0.003), extranasal invasion (p<0.001), and loss of convoluted cerebriform pattern (p=0.002) were identified as independent clinical predictors. The radiomics nomogram model showed excellent calibration and discrimination (AUC: 0.993, 95% confidence interval [CI]: 0.985-1.00 and 0.990, 95% CI: 0.974-1.00) in the training and validation sets, respectively.

CONCLUSION

The nomogram that the combined radiomics signature and clinical risk factors showed a satisfactory ability to predict IP-SCC.

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

BACKGROUND

Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field.

METHODS

In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication.

RESULTS

The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention.

CONCLUSION

As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.

Preoperative Prediction of Sinonasal Inverted Papilloma-associated Squamous Cell Carcinoma (IP-SCC)

INTRODUCTION

Sinonasal inverted papillomas (IP) can undergo transformation into IP-squamous cell carcinomas (IP-SCC). More aggressive treatment plan should be established when IP-SCC is suspected. Nevertheless, inaccuracy of the preoperative punch biopsy results to detect IP-SCC from IP raises the need for an additional strategy. The present study aimed to investigate significant clinicoradiological remarks associated with IP-SCC than IP.

MATERIAL AND METHODS

Postoperative surgical specimens obtained from patients with confirmed IP or IP-SCC at a single tertiary medical center from 1997 to 2018 were retrospectively evaluated. Patients' demographic and clinical characteristics, preoperative in-office punch biopsy results, and preoperative computed tomography (CT) or magnetic resonance images were reviewed. Univariate and multivariate analyses were performed to assess the odds ratio (OR) associated with IP-SCC. The area under the curve (AUC) in the receiver Operating Characteristic (ROC) curve was calculated in the prediction model to discriminate IP-SCC from IP.

RESULTS

The study included 44 IP-SCC and 301 patients with IP. The diagnostic sensitivity of in-office punch biopsy to detect IP-SCC was 70.7%. Multivariate analysis showed that factors significantly associated with IP-SCC included tobacco smoking >10PY (adjusted-OR [aOR]: 4.1), epistaxis (aOR: 3.4), facial pain (aOR: 4.2), bony destruction (aOR: 37.6), bony remodeling (aOR: 36.3), and invasion of adjacent structures (aOR: 31.6) (all p < 0.05). Combining all significantly related clinicoradiological features, the ability to discriminate IP-SCC from IP reached an AUC of 0.974.

CONCLUSION

IP patients with a history of tobacco smoking, facial pain, epistaxis, and bony destruction, remodeling, or invasion of an adjacent structure on preoperative images may be at higher risk for IP-SCC.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:2502-2510, 2023.

Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma

OBJECTIVES

To develop and validate a nomogram based on whole-tumour histograms of apparent diffusion coefficient (ADC) maps for predicting malignant transformation (MT) in sinonasal inverted papilloma (IP).

METHODS

This retrospective study included 209 sinonasal IPs with and without MT, which were assigned into a primary cohort (n = 140) and a validation cohort (n = 69). Eight ADC histogram features were extracted from the whole-tumour region of interest. Morphological MRI features and ADC histogram parameters were compared between the two groups (with and without MT). Stepwise logistic regression was used to identify independent predictors and to construct models. The predictive performances of variables and models were assessed using the area under the curve (AUC). The optimal model was presented as a nomogram, and its calibration was assessed.

RESULTS

Four morphological features and seven ADC histogram parameters showed significant differences between the two groups in both cohorts (all p < 0.05). Maximum diameter, loss of convoluted cerebriform pattern, ADC10th and ADCSkewness were identified as independent predictors to construct the nomogram. The nomogram showed significantly better performance than the morphological model in both the primary (AUC, 0.96 vs 0.88; p = 0.006) and validation (AUC, 0.96 vs 0.88; p = 0.015) cohorts. The nomogram showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram is clinically useful.

CONCLUSIONS

The developed nomogram, which incorporates morphological MRI features and ADC histogram parameters, can be conveniently used to facilitate the pre-operative prediction of MT in IPs.

Differentiation between atypical sinonasal non-Hodgkin's lymphoma and inverted papilloma

AIM

To seek additional magnetic resonance imaging (MRI) features to improve the accuracy of differentiation between atypical sinonasal non-Hodgkin's lymphoma (NHL) and inverted papilloma (IP) using conventional MRI and apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

MRI examinations from 44 atypical cases (21 NHLs and 23 IPs) in sinonasal regions were reviewed retrospectively. Imaging features included tumour laterality, extension, T1-weighted imaging (WI)/T2WI signal intensity homogeneity and ratios, enhancement homogeneity and ratios, and ADC_mean.

RESULTS

In cases of NHL, homogeneous signal intensity was often observed on T2WI, which was homogeneous and significantly less enhanced than the turbinate, with lower ADC_mean. Whereas in IPs, heterogeneous signal intensity was seen on T2WI, which was heterogeneous and of comparable enhancement to the turbinate, and higher ADC_mean values were commonly seen. An ADC_mean cut-off point of 1.10 × 10^-3 mm²/s achieved 100% sensitivity, 90% specificity, and 90% accuracy. In addition, special features were observed that support the distinction between the two tumours, including intestinal pattern enhancement in NHL and spot-like appearance on T2WI and enhancement in IP.

CONCLUSIONS

ADC_mean was the most valuable metric for differentiating between the atypical sinonasal NHLs and IPs.

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

Background and purpose

Inverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy.

Materials and methods

A total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model.

Results

There were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899.

Conclusion

A new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

Preoperative Prediction of Malignant Transformation of Sinonasal Inverted Papilloma Using MR Radiomics

Purpose

Accurate preoperative prediction of the malignant transformation of sinonasal inverted papilloma (IP) is essential for guiding biopsy, planning appropriate surgery and prognosis of patients. We aimed to investigate the value of MRI-based radiomics in discriminating IP from IP-transformed squamous cell carcinomas (IP-SCC).

Methods

A total of 236 patients with IP-SCC (n=92) or IP (n=144) were enrolled and divided into a training cohort and a testing cohort. Preoperative MR images including T1-weighted, T2-weighted, and contrast enhanced T1-weighted images were collected. Radiomic features were extracted from MR images and key features were merged into a radiomic model. A morphological features model was developed based on MR morphological features assessed by radiologists. A combined model combining radiomic features and morphological features was generated using multivariable logistic regression. For comparison, two head and neck radiologists were independently invited to distinguish IP-SCC from IP. The area under the receiver operating characteristics curve (AUC) was used to assess the performance of all models.

Results

A total of 3948 radiomic features were extracted from three MR sequences. After feature selection, we saved 15 key features for modeling. The AUC, sensitivity, specificity, and accuracy on the testing cohort of the combined model based on radiomic and morphological features were respectively 0.962, 0.828, 0.94, and 0.899. The diagnostic ability of the combined model outperformed the morphological features model and also outperformed the two head and neck radiologists.

Conclusions

A combined model based on MR radiomic and morphological features could serve as a potential tool to accurately predict IP-SCC, which might improve patient counseling and make more precise treatment planning.