Diffusion-Weighted Magnetic Resonance Imaging as a Predictor of Outcome in Cervical Cancer After Chemoradiation

Diagnostic Challenges and Long-term Outcomes of Neuroendocrine Carcinoma of the Cervix: A Case Series

Neuroendocrine carcinomas (NECs) of the cervix are rare, aggressive malignancies that are challenging to diagnose and treat. They are high-grade lesions that often share features with poorly differentiated adenocarcinoma and squamous cell carcinoma. NECs are classified into large-cell or small-cell subtypes but can often have a mixed appearance or occur concurrently with a squamous or adenocarcinoma. Diagnosis is dependent on tissue sampling, histomorphology, and immunohistochemistry. Eight cases of NEC were retrieved from the Department of Pathology at our institution from 2008 to 2022. Tumor slides were reviewed and evaluated by 2 independent pathologists. Seven of 8 patients tested positive for neuroendocrine markers, including CD56, synaptophysin, and chromogranin. We discuss the diagnostic challenges, review the histopathology, and describe the treatment courses and clinical outcomes. This case series reveals that traditional markers, such as p16, p63, and p40, may be focally positive in NEC and should not be considered a confirmation of squamous cell carcinoma. Patient outcomes can be affected by delays in diagnosis, misdiagnosis, and inadequate treatment when NEC is not considered in the initial differential diagnosis.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

BACKGROUND

Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients.

METHODS

Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics.

RESULTS

The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively.

CONCLUSION

Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer

Neoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer. We collected MRI images from 72 subjects. Among these subjects, 28 patients (38.9%) belonged to the "Not completely responding" class and 44 patients (61.1%) belonged to the 'Completely responding' class according to their response to treatment. This image set was used for the training and cross-validation of different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic features could be able to capture the disease heterogeneity among the two groups. Three models consisting of three ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbor classifiers) were developed for the binary classification task of interest ("Not completely responding" vs. "Completely responding"), based on supervised learning, using response to treatment as the reference standard. The best model showed an ROC-AUC (%) of 83 (majority vote), 82.3 (mean) [79.9-84.6], an accuracy (%) of 74, 74.1 [72.1-76.1], a sensitivity (%) of 71, 73.8 [68.7-78.9], and a specificity (%) of 75, 74.2 [71-77.5]. In conclusion, our preliminary data support the adoption of a radiomic-based approach to predict the response to neoadjuvant chemotherapy.

Current Update on PET/MRI in Gynecological Malignancies-A Review of the Literature

Early detection of gynecological malignancies is vital for patient management and prolonging the patient's survival. Molecular imaging, such as positron emission tomography (PET)/computed tomography, has been increasingly utilized in gynecological malignancies. PET/magnetic resonance imaging (MRI) enables the assessment of gynecological malignancies by combining the metabolic information of PET with the anatomical and functional information from MRI. This article will review the updated applications of PET/MRI in gynecological malignancies.

FDG PET-CT as an important diagnostic tool and prognostic marker in suspected recurrent cervical carcinoma after radiotherapy: comparison with MRI

BACKGROUND

Recurrent disease in post-irradiation patients with cervical cancer is often difficult to delineate on magnetic resonance imaging (MRI), because posttreatment changes can have a similar appearance, and further evaluation is often required. The aims of the study were to evaluate positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (FDG PET-CT) diagnostic role in suspected recurrent cervical cancer after radiotherapy, compare it to MRI, and assess their prognostic impact in these patients.

PATIENTS AND METHODS

This cohort retrospective study included patients previously treated with radiotherapy for carcinoma of uterine cervix with suspected recurrence, who had undergone MRI of abdomen and pelvis, and were subsequently evaluated on FDG PET-CT, with minimum follow-up period of 12 months.

RESULTS

In the total of 84 patients included in analysis, MRI vs. FDG PET-CT showed sensitivity, specificity and accuracy of 80.1%, 52.4% and 66.7%, vs. 97.6%, 61.9% and 79.8%, respectively. Patients with positive findings on MRI (Log Rank, p = 0.003) and PET-CT (Log Rank, p < 0.001) had shorter progression-free survival (PFS) than those with negative results. In univariate Cox regression models, MRI and FDG PET-CT results were found to be related to PFS (p = 0.005 and p < 0.001, respectively). However, multivariate analysis proved only FDG PET-CT to be independent prognostic factor, where patients with positive FDG PET-CT results had almost nine times higher risk of progression (p < 0.001).

CONCLUSION

FDG PET-CT represents useful diagnostic tool in suspected recurrent cervical cancer after radiotherapy, showing high sensitivity in its detection. In addition, it is an independent factor in predicting progression-free survival in these patients.

Utilization of functional MRI in the diagnosis and management of cervical cancer

INTRODUCTION

Imaging is integral part of cervical cancer management. Currently, MRI is used for staging, follow up and image guided adaptive brachytherapy. The ongoing IQ-EMBRACE sub-study is evaluating the use of MRI for functional imaging to aid in the assessment of hypoxia, metabolism, hemodynamics and tissue structure. This study reviews the current and potential future utilization of functional MRI imaging in diagnosis and management of cervical cancer.

METHODS

We searched PubMed for articles characterizing the uses of functional MRI (fMRI) for cervical cancer. The current literature regarding these techniques in diagnosis and outcomes for cervical cancer were then reviewed.

RESULTS

The most used fMRI techniques identified for use in cervical cancer include diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE). DCE-MRI indirectly reflects tumor perfusion and hypoxia. This has been utilized to either characterize a functional risk volume of tumor with low perfusion or to characterize at-risk tumor voxels by analyzing signal intensity both pre-treatment and during treatment. DCE imaging in these situations has been associated with local control and disease-free survival and may have predictive/prognostic significance, however this has not yet been clinically validated. DWI allows for creation of ADC maps, that assists with diagnosis of local malignancy or nodal disease with high sensitivity and specificity. DWI findings have also been correlated with local control and overall survival in patients with an incomplete response after definitive chemoradiotherapy and thus may assist with post-treatment follow up. Other imaging techniques used in some instances are MR-spectroscopy and perfusion weighted imaging. T2-weighted imaging remains the standard technique used for diagnosis and radiation treatment planning. In many instances, it is unclear what additional information functional-MRI techniques provide compared to standard MRI imaging.

CONCLUSIONS

Functional MRI provides potential for improved diagnosis, prediction of treatment response and prognostication in cervical cancer. Specific sequences such as DCE, DWI and ADC need to be validated in a large prospective setting prior to widespread use. The ongoing IQ-EMBRACE study will provide important clinical information regarding these imaging modalities.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study

OBJECTIVE

We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy.

DESIGN

A retrospective cohort study.

SETTING

Xiangya Hospital of Central South University, Changsha, Hunan, China.

POPULATION

Two hundred and fifty-seven LACC patients who were treated with intensity-modulated radiotherapy from 2014 to 2017.

METHODS

Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction.

MAIN OUTCOMES AND MEASURES

Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia.

RESULTS

A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low-risk and high-risk groups (5-year OS: 95.5% versus 36.4%, p < 0.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%).

CONCLUSION

This study demonstrated that combining clinicopathological parameters with CT-based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.

Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy

PURPOSE

The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT).

MATERIALS AND METHODS

We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon-Mann-Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set.

CONCLUSIONS

The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.

MR-guided radiotherapy for prostate cancer: state of the art and future perspectives

Advances in radiotherapy technology have increased precision of treatment delivery and in some tumour types, improved cure rates and decreased side effects. A new generation of radiotherapy machines, hybrids of an MRI scanner and a linear accelerator, has the potential to further transform the practice of radiation therapy in some cancers. Facilitating superior image quality and the ability to change the dose distribution online on a daily basis (termed "daily adaptive replanning"), MRI-guided radiotherapy machines allow for new possibilities including increasing dose, for hard to treat cancers, and more selective sparing of healthy tissues, where toxicity reduction is the key priority.These machines have already been used to treat most types of cancer, although experience is still in its infancy. This review summarises the potential and current evidence for MRI-guided radiotherapy, with a predominant focus on prostate cancer. Current advantages and disadvantages are discussed including a realistic appraisal of the likely potential to improve patient outcomes. In addition, horizon scanning for near-term possibilities for research and development will hopefully delineate the potential role for this technology over the next decade.

Predicting factors for primary cervical cancer recurrence after definitive radiation therapy

Objectives

The study aimed to retrospectively investigate the apparent diffusion coefficient (ADC) of primary cervical cancer to examine the recurrence correlations in patients treated with radiotherapy (RT).

Methods

The ADC of 31 patients with cervical cancer treated with RT were analyzed as possible risk factors for recurrence. A receiver operating characteristic (ROC) curve of the mean ADC (ADCmean) for the recurrence was generated to determine the cut-off value that yielded optimal sensitivity and specificity. The patient population was subdivided according to the risk factors for recurrence, and the disease-free survival (DFS) was analyzed. The following were investigated to explore the risk factors for recurrence: age, performance status, stage, pelvic lymph node metastasis, histologic tumor grade, maximal diameter of the primary tumor, chemotherapy, and ADCmean.

Results

The median follow-up duration of the patients was 25 months. The recurrence was recognized in 9 (29%) of the 31 cases. The ROC analysis of recurrence showed that the area under the ADCmean curve was 0.889 (95% CI, 0.771-1.000; p = 0.001). The cut-off value of ADC mean was 0.900 × 10^{- 3} mm²/s, with a sensitivity of 86.4% and a specificity of 88.9%. By univariate analysis, the ADCmean was the only factor significantly associated with recurrence.

Conclusion

The ADCmean of the primary tumor is a potential predictive factor for the recurrence in of cervical cancer.

Advances in knowledge

The ADCmean of the primary tumor is a predictor of recurrence in patients with pre-treatment cervical cancer evaluation.

A decade of multi-modality PET and MR imaging in abdominal oncology

OBJECTIVES

To investigate trends observed in a decade of published research on multimodality PET(/CT)+MR imaging in abdominal oncology, and to explore how these trends are reflected by the use of multimodality imaging performed at our institution.

METHODS

First, we performed a literature search (2009-2018) including all papers published on the multimodality combination of PET(/CT) and MRI in abdominal oncology. Retrieved papers were categorized according to a structured labelling system, including study design and outcome, cancer and lesion type under investigation and PET-tracer type. Results were analysed using descriptive statistics and evolutions over time were plotted graphically. Second, we performed a descriptive analysis of the numbers of MRI, PET/CT and multimodality PET/CT+MRI combinations (performed within a ≤14 days interval) performed during a similar time span at our institution.

RESULTS

Published research papers involving multimodality PET(/CT)+MRI combinations showed an impressive increase in numbers, both for retrospective combinations of PET/CT and MRI, as well as hybrid PET/MRI. Main areas of research included new PET-tracers, visual PET(/CT)+MRI assessment for staging, and (semi-)quantitative analysis of PET-parameters compared to or combined with MRI-parameters as predictive biomarkers. In line with literature, we also observed a vast increase in numbers of multimodality PET/CT+MRI imaging in our institutional data.

CONCLUSIONS

The tremendous increase in published literature on multimodality imaging, reflected by our institutional data, shows the continuously growing interest in comprehensive multivariable imaging evaluations to guide oncological practice.

ADVANCES IN KNOWLEDGE

The role of multimodality imaging in oncology is rapidly evolving. This paper summarizes the main applications and recent developments in multimodality imaging, with a specific focus on the combination of PET+MRI in abdominal oncology.

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

Integration of functional imaging in brachytherapy

Functional imaging allows the evaluation of numerous biological properties that could be considered at all steps of the therapeutic management of patients treated with brachytherapy. Indeed, it enables better initial staging of the disease, and some parameters may also be used as predictive biomarkers for treatment response, allowing better selection of patients eligible for brachytherapy. It may also improve the definition of target volumes with the aim of dose escalations by dose-painting. Finally, it could be useful during the follow-up to assess response to treatment. In this review, we report how functional imaging is integrated at the present time during the brachytherapy procedure, and what are its potential future contributions in the main tumour locations where brachytherapy is recommended. Functional imaging has great potential in the contact of brachytherapy, but still, several issues remain to be resolved before integrating it into clinical practice, especially as a biomarker or in dose painting strategies.

Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018

OBJECTIVES

The recommendations cover indications for MRI examination including acquisition planes, patient preparation, imaging protocol including multi-parametric approaches such as diffusion-weighted imaging (DWI-MR),  dynamic contrast-enhanced imaging (DCE-MR) and standardised reporting. The document also underscores the value of whole-body 18-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT) and highlights potential future methods.

METHODS

In 2019, the ESUR female pelvic imaging working group reviewed the revised 2018 FIGO staging system, the up-to-date clinical management guidelines, and the recent imaging literature. The RAND-UCLA Appropriateness Method (RAM) was followed to develop the current ESUR consensus guidelines following methodological steps: literature research, questionnaire developments, panel selection, survey, data extraction and analysis.

RESULTS

The updated ESUR guidelines are recommendations based on ≥ 80% consensus among experts. If ≥ 80% agreement was not reached, the action was indicated as optional.

CONCLUSIONS

The present ESUR guidelines focus on the main role of MRI in the initial staging, response monitoring and evaluation of disease recurrence. Whole-body FDG-PET plays an important role in the detection of lymph nodes (LNs) and distant metastases.

KEY POINTS

• T2WI and DWI-MR are now recommended for initial staging, monitoring of response and evaluation of recurrence. • DCE-MR is optional; its primary role remains in the research setting. • T2WI, DWI-MRI and whole-body FDG-PET/CT enable comprehensive assessment of treatment response and recurrence.

Standardized Uptake Value for 18F-Fluorodeoxyglucose Is a Marker of Inflammatory State and Immune Infiltrate in Cervical Cancer

PURPOSE

Chemoradiotherapy for locally advanced cervical cancer fails in over a third of patients. Biomarkers with therapeutic implications are therefore needed. We investigated the relationship between an established prognostic marker, maximum standardized uptake value (SUV_max) on ¹⁸F-fluorodeoxyglucose positron emission tomography, and the inflammatory and immune state of cervical cancers.

EXPERIMENTAL DESIGN

An SUV_max most prognostic for freedom from progression (FFP) was identified and compared with known prognostic clinical variables in a cohort of 318 patients treated with definitive radiation with prospectively collected clinical data. Gene set enrichment analysis (GSEA) and CIBERSORT of whole-transcriptome data from 68 patients were used to identify biological pathways and immune cell subpopulations associated with high SUV_max. IHC using a tissue microarray (TMA, N = 82) was used to validate the CIBERSORT findings. The impact of macrophages on cervical cancer glucose metabolism was investigated in coculture experiments.

RESULTS

SUV_max <11.4 was most prognostic for FFP (P = 0.001). The GSEA showed that high SUV_max is associated with increased gene expression of inflammatory pathways, including JAK/STAT3 signaling. CIBERSORT and CD68 staining of the TMA showed high SUV_max tumors are characterized by a monocyte-predominant immune infiltrate. Coculture of cervical cancer cells with macrophages or macrophage-conditioned media altered glucose uptake, and IL6 and JAK/STAT3 signaling contribute to this effect.

CONCLUSIONS

SUV_max is a prognostic marker in cervical cancer that is associated with activation of inflammatory pathways and tumor infiltration of myeloid-derived immune cells, particularly macrophages. Macrophages contribute to changes in cervical cancer glucose metabolism.See related commentary by Williamson et al., p. 4136.

Evaluation of diffusion-weighted MRI and geometric distortion on a 0.35T MR-LINAC at multiple gantry angles

Diffusion-weighted imaging (DWI) provides a valuable diagnostic tool for tumor evaluation. Yet, it is difficult to acquire daily MRI data sets in the traditional radiotherapy clinical setting due to patient burden and limited resources. However, integrated MRI radiotherapy treatment systems facilitate daily functional MRI acquisitions like DWI during treatment exams. Before ADC values from MR-RT systems can be used clinically their reproducibility and accuracy must be quantified. This study used a NIST traceable DWI phantom to verify ADC values acquired on a 0.35 T MR-LINAC system at multiple gantry angles. A diffusion-weighted echo planar imaging sequence was used for all image acquisitions, with b-values of 0, 500, 900, 2000 s/mm² for the 1.5 T and 3.0 T systems and 0, 200, 500, 800 s/mm² for the 0.35 T system. Images were acquired at multiple gantry angles on the MR-LINAC system from 0° to 330° in 30° increments to assess the impact of gantry angle on geometric distortion and ADC values. CT images, and three fiducial markers were used as ground truth for geometric distortion measurements. The distance between fiducial markers increased by as much as 7.2 mm on the MR-LINAC at gantry angle 60°. ADC values of deionized water vials from the 1.5 T and 3.0 T systems were 8.30 × 10^-6  mm² /s and -0.85 × 10^-6  mm² /s off, respectively, from the expected value of 1127 × 10^-6  mm² /s. The MR-LINAC system provided an ADC value of the pure water vials that was -116.63 × 10^-6  mm² /s off from the expected value of 1127 × 10^-6  mm² /s. The MR-LINAC also showed a variation in ADC across all gantry angles of 33.72 × 10^-6  mm² /s and 20.41 × 10^-6  mm² /s for the vials with expected values of 1127 × 10^-6  mm² /s and 248 × 10^-6  mm² /s, respectively. This study showed that variation of the ADC values and geometric information on the 0.35 T MR-LINAC system was dependent on the gantry angle at acquisition.

Current Status of Anatomical Magnetic Resonance Imaging in Brachytherapy and External Beam Radiotherapy Planning and Delivery

Radiotherapy planning and delivery have dramatically improved in recent times. Imaging is key to a successful three-dimensional and increasingly four-dimensional based pathway with computed tomography embedded as the backbone modality. Computed tomography has significant limitations for many tumour sites where soft-tissue discrimination is suboptimal, and where magnetic resonance imaging (MRI) has largely superseded in the diagnostic arena. MRI is increasingly used together with computed tomography in the radiotherapy planning pathway and is now established as a prerequisite for several tumours. With the advent of combined MRI and linear accelerator (MR-linac) systems, a transition to MRI-based radiotherapy planning is becoming reality, with increasing experience and research involving these new platforms. In this overview, we aim to highlight how magnetic resonance-guided imaging has improved radiotherapy, using gynaecological malignancies to illustrate, in both external beam radiotherapy and image-guided brachytherapy, and will assess the early evidence for magnetic resonance-guided radiotherapy using combined MR-linac systems.

Early alteration in apparent diffusion coefficient and tumor volume in cervical cancer treated with chemoradiotherapy or radiotherapy: Incremental prognostic value over pretreatment assessments

PURPOSE

Our study aimed to evaluate a prognostic value of early changes in apparent diffusion coefficients (ADC) and tumor volume during treatment in patients with cervical cancer treated with chemoradiotherapy or radiotherapy, and to assess whether the early changes provided an incremental value to pre-treatment ADC and tumor volume in predicting disease recurrences.

METHODS AND MATERIALS

A total of 103 patients with stage IB-IVA cervical cancer including 76 (74%) patients with stage ≥IIIA who underwent magnetic resonance imaging before and during (25 ± 4.6 days after start) the treatment were enrolled. Eighty-one patients received chemoradiotherapy and the remaining 22 had radiotherapy. Both a volumetric ADC and volume of a tumor before and during treatment were measured. %ADC increase and %Volume reduction were defined as changes in the ADCs and tumor volume before and during treatment, respectively.

RESULTS

During a median follow-up of 2.7 years, 42 (41%) patients had disease recurrences. Univariate Cox regression analysis revealed that pre-treatment ADC (Hazard ratio [HR] = 2.8; p = 0.002), %ADC increase (HR = 6.8; p < 0.001), and %Volume reduction (HR = 2.7; p = 0.003) were significant predictors for disease recurrences. On multivariate analysis, %ADC increase was the only independent predictor (adjusted HR = 5.2; p < 0.001) for disease recurrences when adjusted for %Volume reduction and pre-treatment ADC. Global chi-square analysis demonstrated that %ADC increase and %Volume reduction had an additional prognostic value over pre-treatment ADC and tumor volume (p < 0.05). Kaplan-Meier curve analysis showed that both smaller %ADC increase and %Volume reduction were associated with worse prognosis in disease-free survival (log-rank, p < 0.001 and p = 0.002, respectively).

CONCLUSIONS

Among patients with cervical cancer treated with chemoradiotherapy or radiotherapy, early changes in tumor ADCs and tumor volume during treatment are associated with better prognosis. %ADC increase and %Volume reduction during the treatment have an additional prognostic value for predicting tumor recurrence to pre-treatment ADC and tumor volume.

Evaluation and evolution of apparent diffusion coefficient (ADC) in image-guided adaptive brachytherapy (IGABT) for cervical cancer

PURPOSE

Image-guided adaptive brachytherapy (IGABT) recently has shown excellent clinical outcomes with superior local control and less toxicity. For IGABT, T2W (T2-weighted) MRI is the gold standard. However, studies have shown that target delineation with the same results in uncertainties, poor interobserver variabilities, and low conformity indices for high-risk clinical target volume contours. In this study, we investigate the role of diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps to aid in IGABT. We also evaluated ADC from the baseline to brachytherapy.

METHODS AND MATERIALS

Thirty selected patients were enrolled for this study, and two MRIs were taken at diagnosis and before brachytherapy. Patients were divided into two groups, Group 1 being patients with parametrial involvement before external beam radiotherapy and no parametrial involvement before brachytherapy. Group 2 included patients with parametrial involvement before external beam radiotherapy and persistent parametrial involvement before brachytherapy. ADC was measured at the center, edge, and 1 cm from the edge.

RESULTS

The measured ADC increased from diagnosis to brachytherapy, and this increase was more for the patients in Group 1 than in Group 2. The mean TDadc (diagnosis ADC, center), TEadc (tumor edge ADC diagnosis), and T1cmDadc (1 cm from edge at diagnosis) were 0.884, 1.45, and 1.9 × 10^-3 mm²/s, respectively. The TBadc (ADC at brachytherapy, center), TEBadc (tumor edge ADC at brachytherapy), and TE1cmBadc (1 cm from edge brachytherapy) were 1.2, 1.8, and 2.3 × 10^-3 mm²/s, respectively, p-value <0.00001. No abnormal ADC was present outside the high-risk clinical target volume contours.

CONCLUSION

MRI-based IGABT using T2W imaging essentially covers all functionally abnormal zones at brachytherapy. Diffusion-weighted imaging, along with ADC maps, should only be used as a supplement for target delineation.

Advances in MR Imaging of the Female Pelvis

This article focuses on advanced MR imaging techniques of the female pelvis and clinical applications for benign and malignant disease. General and abbreviated protocols for female pelvic MR imaging are reviewed. Diffusion-weighted imaging, dynamic contrast-enhanced MR imaging, and susceptibility-weighted imaging are discussed in the context of adnexal mass characterization using the ADNEx-MR scoring system, evaluation of endometriosis, local staging of cervical and endometrial cancers, assessment of nodal and peritoneal metastasis, and potential detection of leiomyosarcoma. MR defecography is also discussed regarding evaluation of multicompartmental pelvic floor disorders.

Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis

Background

Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients.

Methods

We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31).

Results

For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset.

Conclusions

Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

OBJECTIVE

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images.

METHODS

One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models.

RESULTS

A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort.

CONCLUSIONS

The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC.

KEY POINTS

• Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

Image-Guided Gynecologic Brachytherapy for Cervical Cancer

The incorporation of magnetic resonance imaging in brachytherapy has resulted in an increased use of interstitial catheters in order to create a comprehensive treatment plan that covers the visualized tumor. However, the insertion with passive, image-guidance requires estimating the location of the tumor during the insertion process, rather than visualizing and inserting the catheters directly to the desired location under active tracking. In order to treat residual disease, multiparametric MR sequences can enhance the information available to the clinician. The precision availed by MR-guided brachytherapy results in substantial improvements in needle positioning, and resulting treatment plans.

The transformation of radiation oncology using real-time magnetic resonance guidance: A review

Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both computing and imaging. The direct integration of magnetic resonance imaging (MRI) with linear accelerators represents an exciting development with the potential to dramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an introduction to this topic targeted towards oncologists without a speciality focus in RT. Speciality implications, areas for physician education and research opportunities are identified as they are associated with MRI-guided RT. The uniquely disruptive implications of MRI-guided RT are discussed and placed in context. We further aim to describe and outline important future changes to the speciality of radiation oncology that will occur with MRI-guided RT. The impacts on RT caused by MRI guidance include target identification, RT planning, quality assurance, treatment delivery, training, clinical workflow, tumour response assessment and treatment scheduling. In addition, entirely novel research areas that may be enabled by MRI guidance are identified for future investigation.

Volumetric assessment of apparent diffusion coefficient predicts outcome following chemoradiation for cervical cancer

OBJECTIVE

To determine the utility of volumetric diffusion weighted imaging (DWI) compared to other clinical factors for predicting recurrence and survival in cervical cancer patients treated with definitive chemoradiation.

METHODS AND MATERIALS

We retrospectively studied cervical cancer patients treated with definitive chemoradiation between 2009-2013 at a single institution with a baseline MRI with DWI and 18F-FDG positron emission tomography/computed tomography (FDG-PET) scan. To identify clinical and imaging metrics correlated with survival and recurrence endpoints, variable importance values were calculated from random forest models. To provide clinically relevant threshold values, recursive partitioning analysis dichotomized patients into potential risk groups based on selected metrics. Cox's proportional hazard models assessed the effect of clinical and imaging factors on survival endpoints.

RESULTS

Ninety-three patients were included in the analysis (median age 50 years). At a median follow-up of 35.6 months, 32 patients (34%) had disease recurrence. In the best multivariate model including clinical and imaging parameters, 90th percentile ADC < 1.917 was the only significantly associated factor with worse progression free survival (PFS). Overall survival, PFS, and distant metastasis free survival (DMFS) were significantly different between patient groups divided on 90th percentile ADC with threshold of 1.917 × 10^-3 mm²/s and MRI volume with threshold of 18.9 cc (P = 0.037, P = 0.0002, P = 0.001). High MRI volume and low ADC were associated with worse clinical outcomes.

CONCLUSIONS

Volumetric 90th percentile ADC value of the primary tumor on pretreatment MRI was a significant predictor of PFS and DMFS in cervical cancer patients, independent of established clinical factors and SUV on FDG-PET.

Prognostic value of ADC quantification for clinical outcome in uterine cervical cancer treated with concurrent chemoradiotherapy

OBJECTIVES

To investigate the prognostic value of diffusion-weighted imaging (DWI) in predicting clinical outcome in patients with cervical cancer after concurrent chemoradiotherapy (CCRT).

METHODS

We enrolled 124 cervical cancer patients who received definitive CCRT and underwent 3 T-MRI before and 1 month after initiating treatment. The mean apparent diffusion coefficient (ADC) value was measured on the tumor and the changes in ADC percentage (ΔADC_mean) between the two time points were calculated. The Cox proportion hazard model was used to evaluate the associations between imaging or clinical variables and progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS).

RESULTS

In multivariate analysis, ΔADC_mean was the only independent predictor of PFS (hazard ratio [HR] = 0.2379, p = 0.005), CSS (HR = 0.310, p = 0.024), and OS (HR = 0.217, p = 0.002). Squamous cell carcinoma antigen, histology, and pretreatment tumor size were significantly independent predictors of PFS. Tumor size response was significantly independent predictor of CSS and OS. Using the cutoff values of ΔADC_mean, the PFS was significantly lower for ΔADC_mean < 27.8% (p = 0.001). The CSS and OS were significantly lower for ΔADC_mean < 16.1% (p = 0.002 and p < 0.001, respectively).

CONCLUSION

The percentage change in tumor ADC may be a useful predictor of disease progression and survival in patients with cervical cancer treated with CCRT.

KEY POINTS

• DWI is widely used as a potential marker of tumor viability. • Percentage change in tumor ADC (ΔADC _mean ) was an independent marker of PFS, CSS, and OS. • Survival was better in patients with ≥ ΔADC _mean cutoff value than with < the cutoff value.

Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator

PURPOSE

Systems for magnetic resonance (MR-) guided radiotherapy enable daily MR imaging of cancer patients during treatment, which is of interest for treatment response monitoring and biomarker discovery using quantitative MRI (qMRI). Here, the performance of a 1.5 T MR-linac regarding qMRI was assessed on phantoms. Additionally, we show the feasibility of qMRI in a prostate cancer patient on this system for the first time.

MATERIALS AND METHODS

Four 1.5 T MR-linac systems from four institutes were included in this study. T₁ and T₂ relaxation times, and apparent diffusion coefficient (ADC) maps, as well as dynamic contrast enhanced (DCE) images were acquired. Bland-Altman statistics were used, and accuracy, repeatability, and reproducibility were determined.

RESULTS

Median accuracy for T₁ ranged over the four systems from 2.7 to 14.3%, for T₂ from 10.4 to 14.1%, and for ADC from 1.9 to 2.7%. For DCE images, the accuracy ranged from 12.8 to 35.8% for a gadolinium concentration of 0.5 mM and deteriorated for higher concentrations. Median short-term repeatability for T₁ ranged from 0.6 to 5.1%, for T₂ from 0.4 to 1.2%, and for ADC from 1.3 to 2.2%. DCE acquisitions showed a coefficient of variation of 0.1-0.6% in the signal intensity. Long-term repeatability was 1.8% for T₁, 1.4% for T₂, 1.7% for ADC, and 17.9% for DCE. Reproducibility was 11.2% for T₁, 2.9% for T₂, 2.2% for ADC, and 18.4% for DCE.

CONCLUSION

These results indicate that qMRI on the Unity MR-linac is feasible, accurate, and repeatable which is promising for treatment response monitoring and treatment plan adaptation based on daily qMRI.

Recent advances in gynecologic radiation oncology

PURPOSE OF REVIEW

Radiation has long been a primary treatment modality in locally advanced gynecologic carcinomas and a tool for palliation of metastatic disease. Here we review advances in imaging and radiation technologies and the corresponding clinical evolution of the role for radiation oncology in the treatment of gynecologic malignancies.

RECENT FINDINGS

Advances in MRI are impacting diagnosis, radiation target delineation, planning, treatment delivery, and response assessment. The combination of MRI and PET-computed tomography (PET-CT) has the potential to significantly change treatment recommendations as compared with a single imaging modality. Advances in radiation delivery including intensity modulated radiation therapy (IMRT) and stereotactic ablative radiotherapy (SABR) have expanded the role for radiation in the definitive treatment of high risk and oligometastatic ovarian cancer. Finally, global disparities in oncology resources has led to the development of resource stratified treatment guidelines for gynecologic malignancies. The use of ultrasound for brachytherapy planning may help improve access to this critical treatment modality.

SUMMARY

As imaging and radiation technologies advance, the indications for radiation in the treatment of gynecologic malignancies continues to evolve. Here we review the changing landscape of radiation oncology in the multidisciplinary management of gynecologic cancers.

Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions

Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.

Spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography and magnetic resonance diffusion imaging metrics in cervical cancer

Background

This study investigated the spatial relationship of 2-deoxy-2-[¹⁸F]-fluoro-D-glucose positron emission tomography ([¹⁸F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data.

We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [¹⁸F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUV_max), mean ADC (ADC_mean), and mean tumor-to-muscle ADC ratio (ADC_T/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis.

Results

Seventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUV_max and ADC_mean, and SUV_max and ADC_T/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS).

Conclusions

In this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0403-7) contains supplementary material, which is available to authorized users.

Pre-treatment DWI as a predictor of overall survival in locally advanced pancreatic cancer treated with Cyberknife radiotherapy and sequential S-1 therapy

Background

To identify the value of the pre-treatment apparent diffusion coefficient (ADC) derived from diffusion weighted imaging (DWI) in predicting the overall survival (OS) for locally advanced pancreatic cancer (LAPC) treated with Cyberknife followed by sequential S-1 chemotherapy.

Methods

Patients with UICC-T4 LAPC who underwent DWI scan (3.0 Tesla) using two b-values (0, 600 s/mm²) in our center between 2015 and 2017 were enrolled. Mean ADCs of the region of interest (ROI) drawn manually on DWI imaging were measured by two independent radiologists at an interval of 1 month. The association between prognostic factors and patient survival was determined using univariate and multivariate analyses. Cox proportional hazard model was used for identification of independent prognostic factors of OS.

Results

A total of 41 patients (28 males and 13 females) were included, with a median age of 64 years, with 5 patients (3 males and 2 females) lost. The median OS was 11.7 months (range 2.8–23.3) among all 41 patients. The 1-year OS was 46% (95% CI 30%–62%). Univariate and multivariate analyses indicated that pre-treatment ADC value (HR 10.652, P = 0.0093), age (HR 0.952, P = 0.015), CA19–9 (HR 1.001, P = 0.0022) and administration of S-1 (HR 0.128, P = 0.0002) were independent predicting factors of OS.

Conclusion

The mean ADC value of the primary tumor on pre-treatment DWI imaging was an independent predictor of OS in patients with LAPC receiving Cyberknife followed by sequential S-1.

Advanced imaging to predict response to chemotherapy in colorectal liver metastases - a systematic review

BACKGROUND

The assessment of colorectal liver metastases (CRLM) after treatment with chemotherapy is challenging due to morphological and/or functional change without changes in size. The aim of this review was to assess the value of FDG-PET, FDG-PET-CT, CT and MRI in predicting response to chemotherapy in CRLM.

METHODS

A systematic review was undertaken based on PRISMA statement. PubMed and Embase were searched up to October 2016 for studies on the accuracy of PET, PET-CT, CT and MRI in predicting RECIST or metabolic response to chemotherapy and/or survival in patients with CRLM. Articles evaluating the assessment of response after chemotherapy were excluded.

RESULTS

Sixteen studies met the inclusion criteria and were included for further analysis. Study results were available for 6 studies for FDG-PET(-CT), 6 studies for CT and 9 studies for MRI. Generally, features predicting RECIST or metabolic response often predicted shorter survival. The ADC (apparent diffusion coefficient, on MRI) seems to be the most promising predictor of response and survival. In CT-related studies, few attenuation-related parameters and texture features show promising results. In FDG-PET(-CT), findings were ambiguous.

CONCLUSION

Radiological data on the prediction of response to chemotherapy for CRLM is relatively sparse and heterogeneous. Despite that, a promising parameter might be ADC. Second, there seems to be a seemingly counterintuitive correlation between parameters that predict a good response and also predict poor survival.

Diffusion-weighted MRI-derived ADC values reflect collagen I content in PDX models of uterine cervical cancer

Apparent diffusion coefficient (ADC) values derived from diffusion-weighted magnetic resonance imaging (DW-MRI) are known to reflect the cellular environment of biological tissues. However, emerging evidence accentuates the influence of stromal elements on ADC values. The current study sought to elucidate whether a correlation exists between ADC and the fraction of collagen I-positive tissue across different tumor models of uterine cervical cancer. Early and late generation tumors of four patient-derived xenograft (PDX) models of squamous cell carcinoma (BK-12, ED-15, HL-16, and LA-19) were included. DW-MRI was performed with diffusion encoding constants (b) of 200, 400, 700, and 1000 s/mm2 and diffusion gradient sensitization in three orthogonal directions. The fraction of collagen I-positive connective tissue was determined by immunohistochemistry. Mono-exponential decay curves, from which the ADC value of tumor voxels was calculated, yielded good fits to the diffusion data. A significant inverse correlation was detected between median tumor ADC and collagen I fraction across the four PDX models, indicating that collagen fibers in the extracellular space have the ability to inhibit the movement of water molecules in these xenografts. The results encourage further exploration of DW-MRI as a non-invasive imaging method for characterizing the stromal microenvironment of tumors.

Role of Functional Magnetic Resonance Imaging Derived Parameters as Imaging Biomarkers and Correlation with Clinicopathological Features in Carcinoma of Uterine Cervix

INTRODUCTION

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool in the evaluation and management of cervical cancer. The role of Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) as a non-invasive imaging biomarker is promising in characterization of the tumour and prediction of response.

AIM

The aim of this study was to evaluate the role of conventional MRI and diffusion weighted MRI in predicting clinicopathological prognostic factors.

MATERIALS AND METHODS

This was a retrospective study. The data of 100 cervical cancer patients who had MRI with DWI was retrieved from the database and analysed. Clinico pathological details were collected from the computerized hospital information system. SPSS version 15.0 was used for statistical analysis.

RESULTS

The mean tumour dimensions on MRI in x, y and z axes were 43.04 mm (±13.93, range: 17-85), 37.05mm (±11.83, range: 9-80) and 39.63 mm (±14.81, range: 14 -76). The mean T₂W MRI based tumour volume (TV) was 48.18 (±34.3, range: 7-206) and on DWI images was 36.68(±33.72, range: 2.5-200). The mean ADC value in patients with squamous cell carcinoma was 0.694 (±0.125, n=88), adenocarcinoma was 0.989 (±0.309, n=6), adenosquamous was 0.894 (±0.324, n=4). There was statistical significant difference in mean ADC between squamous vs. non squamous histology (p = 0.02). The mean ADC values of well differentiated, moderately differentiated, and poorly differentiated tumours were 0.841(±0.227, n= 26), 0.729 (±0.125, n=28), 0.648 (±0.099, n=46) respectively. There was significant statistical difference of mean ADC between well differentiated, moderately differentiated (p=0.020) and poorly differentiated tumours (p=0.0001). Difference between the mean ADC values between the node positive and node negative disease was statistically significant (p=0.0001). There was no correlation between the tumour volumes on T₂W and DWI images and ADC values. Sixteen patients had residual/recurrent disease at a median follow up of 12 months (range: 3-59 months). The mean ADC values in this group was 0.71 (n=16) and was not significantly different from the disease free group (mean ADC =0.72, n=74).

CONCLUSION

Higher ADC values are associated with favourable histology and differentiation. Adenocarcinomas have higher ADC values followed by adenosquamous followed by squamous cell carcinomas. Well differentiated tumours had higher ADC values than moderately followed by poorly differentiated tumours. DWI with ADC have a potential role as an imaging biomarker for prognostication and needs further studies for routine clinical applications.