Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN).

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.

RETRACTED: Di Paola et al. Assessment of 2-Pentadecyl-2-oxazoline Role on Lipopolysaccharide-Induced Inflammation on Early Stage Development of Zebrafish (Danio rerio). Life 2022, 12, 128.

The journal retracts the article, "Assessment of 2-Pentadecyl-2-oxazoline Role on Lipopolysaccharide-Induced Inflammation on Early Stage Development of Zebrafish (Danio rerio)" [...].

The Impact of Standing Electric Scooters on Maxillofacial Fractures: An Italian Multi-Centric Epidemiological Study.

Objectives: This study aimed to determine the impact of standing electric scooters on maxillofacial on the Italian territory. Methods: The authors analyzed the epidemiology of the injuries to define electric mobility's impact on maxillofacial surgery practice. For this retrospective cohort study, data were collected by unifying the standing e-scooter-related fractures database from 10 Italian maxillofacial surgery departments. The reference period considered was from January 2020 to December 2023. The main data considered included age, gender, type of access, time slot of admission, type of admission, alcohol level, helmet use, dynamics of the accident, and area of the fracture. Results: A total of 79 patients were enrolled. The average age of the participants was approximately 31 years. The blood alcohol level was found to be above the Italian norm in 15 cases (19%). Only one patient wore a helmet. The most affected facial third was the middle one with 36 cases (45.5%), followed by the lower one (31, 39.3%). The most recurrent patterns were fractures of the orbito-malar-zygomatic complex (15, 19%), followed by multifocal (bifocal, trifocal) fractures of the mandible (14, 17.5%). Conclusions: This study demonstrated how maxillofacial fractures related to the use of electric scooters are associated with complex patterns, associated with a high rate of post-surgical aftermaths.

A Physiological-Informed Generative Model for Improving Breast Lesion Classification in Small DCE-MRI Datasets.

In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.

Impaired mitochondrial quality control in fibromyalgia: Mechanisms involved in skeletal muscle alteration.

Fibromyalgia (FMS) is a persistent syndrome marked by widespread musculoskeletal pain and behavioural symptoms. Given the hypothesis linking FMS aetiology to mitochondrial dysfunction and oxidative stress, we examined the biochemical correlation among these factors by studying specific proteins associated with mitochondrial homeostasis in muscle. Additionally, this study investigated the role of Boswellia serrata gum resin extract (BS), known for its various functions, including the potent induction of antioxidant enzymes, in determining protective or reparative mechanisms in the muscle cells. Sprague-Dawley rats were injected with reserpine to induce FMS. These animals exhibited moderate changes in hind limb skeletal muscles, experiencing mobility difficulties. Additionally, there were noteworthy morphological and ultrastructural alterations, along with the expression of myogenin, mitochondrial enzymes and oxidative stress markers in the gastrocnemius muscle. Interestingly, BS demonstrated a reduction in spontaneous motor activity difficulties. Moreover, BS showed a positive impact on musculoskeletal morphostructural aspects, as well as a decrease in oxidative stress and mitochondrial alterations. In particular, BS restored the mRNA expression of citrate synthase and cytochrome-c oxidase subunit II and the activity of electron transfer chain complexes. BS also influenced mitochondrial biogenesis, upregulating PGC-1α expression and the related transcription factors (Nrf1, Tfam, Nrf2, FOXO3a, SIRT3, GCLC, NQO1, SOD2 and GPx4), oxidative stress (lipid peroxidation, GSH levels and GSH-Px activity) and mitochondrial dynamics and function (Mnf2 expression and CoQ10 levels). Overall, this study underlined the key role of the mitochondrial alteration in FMS and that BS had a very high antioxidant effect in these organelles and also in the cells.

Enviromental endocrine disruptor risks in the central nervous system: Neurotoxic effects of PFOS and glyphosate.

Endocrine disruptors (EDs) pose significant risks to human and environmental health, with potential implications for neurotoxicity. This study investigates the synergistic neurotoxic effects of perfluorooctane sulfonate (PFOS) and glyphosate (GLY), two ubiquitous EDs, using SHSY5Y neuronal and C6 astrocytic cell lines. While individual exposures to PFOS and glyphosate at non-toxic concentrations did not induce significant changes, their combination resulted in a marked increase in oxidative stress and neuroinflammatory responses. Specifically, the co-exposure led to elevated levels of interleukin-6, tumor necrosis factor alpha, and interferon gamma, along with reduced interleukin-10 expression, indicative of heightened neuroinflammatory processes. These findings underscore the importance of considering the synergistic interactions of EDs in assessing neurotoxic risks and highlight the urgent need for further research to mitigate the adverse effects of these compounds on neurological health.

Correction: Ionna et al. Sentinel Lymph Node Biopsy (SLNB) for Early-Stage Head and Neck Squamous-Cell Carcinoma of the Tongue: Twenty Years of Experience at I.N.T. "G.Pascale". Cancers 2024, 16, 1153.

In the published publication [...].

Total mesorectal excision after rectal-sparing approach in locally advanced rectal cancer patients after neoadjuvant treatment: a high volume center experience.

Background: In patient with a complete or near-complete clinical response after neoadjuvant treatment for locally advanced rectal cancer, the organ-sparing approach [watch & wait (W&W) or local excision (LE)] is a possible alternative to major rectal resection. Although, in case of local recurrence or regrowth, after these treatments, a total mesorectal excision (TME) can be operated. Method: In this retrospective study, we selected 120 patients with locally advanced rectal cancer (LARC) who had a complete or near-complete clinical response after neoadjuvant treatment, from June 2011 to June 2021. Among them, 41 patients were managed by W&W approach, whereas 79 patients were managed by LE. Twenty-three patients underwent salvage TME for an unfavorable histology after LE (11 patients) or a local recurrence/regrowth (seven patients in LE group - five patients in W&W group), with a median follow-up of 42 months. Results: Following salvage TME, no patients died within 30 days; serious adverse events occurred in four patients; 8 (34.8%) patients had a definitive stoma; 8 (34.8%) patients undergone to major surgery for unfavorable histology after LE - a complete response was confirmed. Conclusion: Notably active surveillance after rectal sparing allows prompt identifying signs of regrowth or relapse leading to a radical TME. Rectal sparing is a possible strategy for LARC patients although an active surveillance is necessary.

Role of mitochondrial dysfunction and biogenesis in fibromyalgia syndrome: Molecular mechanism in central nervous system.

A critical role for mitochondrial dysfunction has been shown in the pathogenesis of fibromyalgia. It is a chronic pain syndrome characterized by neuroinflammation and impaired oxidative balance in the central nervous system. Boswellia serrata (BS), a natural polyphenol, is a well-known able to influence the mitochondrial metabolism. The objective of this study was to evaluate the mitochondrial dysfunction and biogenesis in fibromyalgia and their modulation by BS. To induce the model reserpine (1 mg/Kg) was subcutaneously administered for three consecutive days and BS (100 mg/Kg) was given orally for twenty-one days. BS reduced pain like behaviors in reserpine-injected rats and the astrocytes activation in the dorsal horn of the spinal cord and prefrontal cortex that are recognized as key regions associated with the neuropathic pain. Vulnerability to neuroinflammation and impaired neuronal plasticity have been described as consequences of mitochondrial dysfunction. BS administration increased PGC-1α expression in the nucleus of spinal cord and brain tissues, promoting the expression of regulatory genes for mitochondrial biogenesis (NRF-1, Tfam and UCP2) and cellular antioxidant defence mechanisms (catalase, SOD2 and Prdx 3). According with these data BS reduced lipid peroxidation and the GSSG/GSH ratio and increased SOD activity in the same tissues. Our results also showed that BS administration mitigates cytochrome-c leakage by promoting mitochondrial function and supported the movement of PGC-1α protein into the nucleus restoring the quality control of mitochondria. Additionally, BS reduced Drp1 and Fis1, preventing both mitochondrial fission and cell death, and increased the expression of Mfn2 protein, facilitating mitochondrial fusion. Overall, our results showed important mitochondrial dysfunction in central nervous system in fibromyalgia syndrome and the role of BS in restoring mitochondrial dynamics.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.

This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (DCs), Odontogenic Keratocysts (OKCs), and Unicystic Ameloblastomas (UAs). This retrospective study involves 103 patients who underwent jaw lesion surgery in the Maxillofacial Surgery Unit of Federico II University Of Naples between January 2018 and January 2023. Nonparametric Wilcoxon-Mann-Whitney and Kruskal Wallis tests were used for continuous variables. Linear and non-logistic regression models (LRM and NLRM) were employed, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarkers achieved an accuracy of 95 % and an AUC of 0.96. The accuracy of single radiomics predictors was lower than that of inflammatory biomarkers, with an AUC of 0.83. The Fine Tree model, utilizing NLR, SII, and one radiomic feature, achieved an accuracy of 94.3 % (AUC = 0.95) on the training and testing sets, and a validation set accuracy of 100 %. The Fine Tree model demonstrated the capability to discriminate between OKCs, UAs, and DCs. However, the LRM utilizing four inflammatory biomarkers proved to be the most effective algorithm for distinguishing between OKCs, UAs, and DCs.

Correction: D'Amico et al. Toxic Exposure to Endocrine Disruptors Worsens Parkinson's Disease Progression through NRF2/HO-1 Alteration. Biomedicines 2022, 10, 1073.

Errors in Figures [...].

Translational Insights in the Landscape of Salivary Gland Cancers: Ready for a New Era?

Salivary gland carcinomas (SGCs) are rare neoplasms, representing less than 10% of all head and neck tumors, but they are extremely heterogeneous from the histological point of view, their clinical behavior, and their genetics. The guidelines regarding their treatment include surgery in most cases, which can also play an important role in oligometastatic disease. Where surgery cannot be used, systemic therapy comes into play. Systemic therapy for many years has been represented by polychemotherapy, but recently, with the affirmation of translational research, it can also count on targeted therapy, at least in some subtypes of SGCs. Interestingly, in some SGC histotypes, predominant mutations have been identified, which in some cases behave as "driver mutations", namely mutations capable of governing the carcinogenesis process. Targeting these driver mutations may be an effective therapeutic strategy. Nonetheless, it is not always possible to have drugs suitable for targeting driver mutations-and targeting driver mutations is not always accompanied by a clinical benefit. In this review, we will analyze the main mutations predominant in the various histotypes of SGCs.

Sentinel Lymph Node Biopsy (SLNB) for Early-Stage Head and Neck Squamous-Cell Carcinoma of the Tongue: Twenty Years of Experience at I.N.T. "G.Pascale".

Oral tongue squamous-cell carcinoma (OTSCC) is the most prevalent malignancy in the head and neck region. Lymphatic spread, particularly to cervical lymph nodes, significantly impacts 5-year survival rates, emphasizing the criticality of precise staging. Metastatic cervical lymph nodes can decrease survival rates by 50%. Yet, elective neck dissection (END) in T1-2 cN0 patients proves to be an overtreatment in around 80% of cases. To address this, sentinel lymph node biopsy (SLNB) was introduced, aiming to minimize postoperative morbidity. This study, conducted at the ENT and Maxillofacial Surgery department of the Istituto Nazionale Tumori in Naples, explores SLNB's efficacy in early-stage oral tongue squamous-cell carcinoma (OTSCC). From January 2020 to January 2022, 122 T1/T2 cN0 HNSCC patients were enrolled. Radioactive tracers and lymphoscintigraphy identified sentinel lymph nodes, aided by a gamma probe during surgery. Results revealed 24.6% SLN biopsy positivity, with 169 SLNs resected and a 21.9% positivity ratio. The study suggests SLNB's reliability for T1-2 cN0 OTSCC patient staging and early micrometastasis detection.

Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. METHODS: Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. RESULTS: Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization. CONCLUSIONS: AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.

Oxidative stress and mitochondrial dysfunction in brain of vinclozolin exposed animals.

Vinclozolin (VCZ) is a widely used fungicide in agriculture, especially in fruits and wine. Various studies have detailed the effects of VCZ exposure on different organs, but no information is available on its effects on brain tissues. This paper investigated the effects of VCZ exposure on the oxidative stress and mitochondrial dysfunction in brain tissue. C57BL/6 mice were exposed to VCZ (100 mg/kg) by oral gavage for 28 days. Mitochondrial homeostasis, often known as mitochondrial quality control, involves a range of processes, including mitochondrial biogenesis, mitochondrial fusion and fission, mitophagy and autophagy. VCZ administration modified the mRNA expression levels of Sirt1, Sirt3, PGC-1α, TFAM, Nrf1, VDAC-1 and Cyt c in brain tissue, as compared to control animals (CTR). The analyses also showed increased oxidative stress, in particular VCZ administration reduced SOD and CAT activities and GSH levels while increased T-AOC levels and lipid peroxidation. Additionally, brain tissues from VCZ group showed DNA oxidation (increased PARP-1 immunostaining) and apoptosis (increased TUNEL+ cells, increased expression of Bax mRNA level and reduced Bcl-2 levels). Western blot and immunohistochemical analyses showed increased mitophagic pathway with the accumulation of PINK1 and Parkin in mitochondria. Additionally, autophagic pathway was also increased with the increased expression and colocalization of LC3 with Neun and GFAP. Overall, this study showed that chronic VCZ exposure impaired mitochondrial homeostasis and increased oxidative stress in brain tissues.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.