MRI for assessing and predicting response to neoadjuvant treatment in rectal cancer

Volumetric histogram analysis of amide proton transfer-weighted imaging for predicting complete tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal adenocarcinoma

OBJECTIVES

To investigate the potential of histogram analysis applied to pre-treatment amide proton transfer-weighted (APTw) imaging in predicting complete pathological regression (pCR) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT).

MATERIALS AND METHODS

This retrospective study enrolled LARC patients who underwent preoperative rectal magnetic resonance imaging (MRI). Based on histologic assessment, the patients were divided into a pathological complete response (pCR) group or a non-pCR group. APTw histogram features, apparent diffusion coefficient (ADC), and clinical parameters were analyzed. Mann-Whitney U-test, Spearman rank correlation, and univariate and multivariate logistic regression were used for statistical analysis. The predictive performances of different models were evaluated by the receiver operating characteristic curve (ROC).

RESULTS

One-hundred forty-five patients were included (mean age, 61.6 years ± 11.8 [SD]; 87 men). pCR patients exhibited lower pre-treatment ADC value, higher pre-treatment APTw-10%, APTw-90%, minimum, maximum, median, mean, range, and root mean square (RMS) of the primary tumor compared to non-pCR patients (all p < 0.05). APTw-10%, APTw-90%, maximum, mean, median, minimum, range, and RMS showed negative correlations with the tumor regression grade (TRG) category (r ranged between -0.457 and -0.173; all p < 0.005). Skewness, kurtosis, and entropy exhibited positive correlations with the TRG category (r = 0.278, 0.319, and 0.324, respectively; all p < 0.05). The combined model had a higher AUC of 0.930, with 93.9% sensitivity and 83.9% specificity.

CONCLUSION

Histogram analysis of pre-treatment APTw may hold promise as a novel approach for predicting the response of LARC patients to nCRT.

KEY POINTS

Question Predicting response to nCRT is crucial for early stratified management of LARC patients; however, current radiological studies remain inconclusive. Finding LARC patients with pCR is correlated with higher pre-treatment APTw intensity-related and lower shape-related histogram features. Clinical relevance The APTw-histogram model and the APTw-clinical combined model demonstrated strong diagnostic efficacy and clinical practicality in predicting LARC patients' responsiveness to nCRT, offering new insights for early clinical decision-making.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceae bacterium and Blautia wexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

MRI-based radiomics for stratifying recurrence risk of early-onset rectal cancer: a multicenter study

BACKGROUND

Early-onset rectal cancer (EORC) is characterized by a unique disease process with different clinicopathological features compared with late-onset rectal cancer (LORC). Research on the risk of recurrence in EORC patients, however, is limited. We aim to develop a predictive model to accurately predict EORC recurrence risk.

MATERIALS AND METHODS

Rectal cancer patients who underwent radical surgery and T2-weighted imaging and diffusion-weighted imaging magnetic resonance imaging (MRI) were retrospectively enrolled from three medical institutions from November 2012 to November 2018. Differences in clinicopathological characteristics between EORC and LORC were compared. Five prediction models for disease-free survival were constructed based on clinicopathological variables and five radiomic features from pretreatment MRI of the EORC. A fixed cut-off value calculated in the training set was used to stratify EORC patients into high-risk and low-risk groups of post-operative recurrence. Model performance was evaluated by concordance index (C-index) and receiver operating characteristic curve.

RESULTS

A total of 264 EORC patients (median age, 43 years, 163 males) and 778 LORC patients (median age, 62 years, 520 males) were enrolled. Pretreatment positive carcinoembryonic antigen [hazard ratio (HR) = 2.84, P = 0.006], pathological positive lymph node status (pN positive) [HR = 2.86, P = 0.011] and MRI-based radiomics score [HR = 2.72, P < 0.001] are independent risk factors for disease-free survival in EORC patients. The EORC-ClinPathRadiom model, constructed by integrating the clinicopathological characteristics and MRI-based radiomics features of EORC, showed C-index of 0.82, 0.82, and 0.81 in the training, internal, and external test sets, respectively. This model effectively stratified EORC patients into high risk and low risk of recurrence (HRs for the training, internal, and external test sets were 8.96, 6.81, and 7.46, respectively).

CONCLUSION

The EORC-ClinPathRadiom model can effectively predict and stratify the risk of post-operative recurrence in EORC patients.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

PURPOSE

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

METHODS

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

RESULTS

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

CONCLUSION

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study

PURPOSE

To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer.

METHODS

This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram.

RESULTS

The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram.

CONCLUSION

The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.

Prognostic factors for local recurrence in patients with rectal cancer submitted to neoadjuvant chemoradiotherapy and total mesorectal excision

Prognostic factors for local recurrence in patients with rectal cancer submitted to neoadjuvant chemoradiotherapy and total mesorectal excision.

BACKGROUND

The standard curative treatment for locally advanced rectal cancer of the middle and lower thirds is long-course chemoradiotherapy followed by total mesorectal excision.

PURPOSE

To evaluate the prognostic factors associated with local recurrence in patients with rectal cancer submitted to neoadjuvant chemoradiotherapy and total mesorectal excision.

METHODS

Retrospective study including patients with rectal cancer T3-4N0M0 or T (any)N + M0 located within 10 cm from the anal border, or patients with T2N0M0 located within 5 cm, treated by long course chemoradiotherapy followed by total mesorectal excision with curative intent. Clinical, demographic, radiologic, surgical, and anatomopathological data were collected. Local recurrence was estimated using the Kaplan-Meier function, and risk was estimated according to each characteristic using univariate and multivariate analyses.

RESULTS

270 patients were included, 57.8% male and mean age 61.7 (30‒88) years. At initial staging, 6.7% of patients were stage I, 21.5% stage II, and 71.8% stage III. Open surgery was performed in 65.2%, with sphincter preservation in 78.1%. Mortality within 30 postoperative days was 0.7%. After 49.4 (0.5‒86.1) months of median follow-up, overall and local recurrences were 26.3% and 5.9%. On multivariate analyses, local recurrence was associated with involvement of the mesorectal fascia on restaging MRI (HR = 9.11, p = 0.001) and with pathologic involvement of radial surgical margin (HR = 8.19, p < 0.001).

CONCLUSION

Local recurrence of rectal cancer treated with long-course chemoradiation and total mesorectal excision is low and is associated with pathologic involvement of the radial surgical margin and can be predicted on restaging MRI.

Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: A narrative review

Neoadjuvant chemoradiotherapy is the standard treatment for patients with locally advanced rectal cancers owing to its ability to downstage primary tumours. Some patients can achieve pathological complete response after neoadjuvant therapy, and can adopt a "watch and wait" treatment strategy to avoid overtreatment. Therefore, it is essential to develop strategies for predicting responses to neoadjuvant therapy. Radiomics has shown great potential in extracting tumour features from high-throughput medical images for the construction of mathematics models for predicting the effects of anticancerous therapies. Herein, we explored MRI-based radiomics and found that it can predict responses of locally advanced rectal cancers to chemoradiation. Efficient radiomics model allow early-stage prediction of the effect of neoadjuvant chemoradiotherapy on locally advanced rectal cancers. It helps clinicians to make informed therapeutic decisions. In this review, we discuss the workflow of radiomics, and summarize the clinical application of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer.

Optimal debulking surgery in ovarian cancer patients: MRI may predict the necessity of rectosigmoid resection

OBJECTIVES

To determine whether MRI can predict the necessity of rectosigmoid resection (RR) for optimal debulking surgery (ODS) in ovarian cancer (OC) patients and to compare the predictive accuracy of pre- and post-neoadjuvant chemotherapy (NACT) MRI.

METHODS

The MRI of 82 OC were retrospectively analyzed, including six bowel signs (length, transverse axis, thickness, circumference, muscularis involvement, and submucosal edema) and four para-intestinal signs (vaginal, parametrial, ureteral, and sacro-recto-genital septum involvement). The parameters reflecting the degree of muscularis involvement were measured. Patients were divided into non-RR and RR groups based on the operation and postoperative outcomes. The independent predictors of the need for RR were identified by multivariate logistic regression analysis.

RESULTS

Imaging for 82 patients was evaluated (67 without and 15 with NACT). Submucosal edema and muscularis involvement (OR 13.33 and 8.40, respectively) were independent predictors of the need for RR, with sensitivities of 83.3% and 94.4% and specificities of 93.9% and 81.6%, respectively. Among the parameters reflecting the degree of muscularis involvement, circumference ≥ 3/12 had the highest prediction accuracy, increasing the specificity from 81.6% for muscularis involvement only to 98.0%, with only a slight decrease in sensitivity (from 94.4% to 88.9%). The predictive sensitivities of pre-NACT and post-NACT MRI were 100.0% and 12.5%, respectively, and the specificities were 85.7% and 100.0%, respectively.

CONCLUSIONS

MRI analysis of rectosigmoid muscularis involvement and its circumference can help predict the necessity of RR in OC patients, and pre-NACT MRI may be more suitable for evaluation.

CRITICAL RELEVANCE STATEMENT

We analyzed preoperative pelvic MRI in OC patients. Our findings suggest that MRI has predictive potential for identifying patients who require RR to achieve ODS.

KEY POINTS

The need for RR must be determined to optimize treatment for OC patients. Muscularis involvement circumference ≥ 3/12 could help predict RR. Pre-NACT MRI may be superior to post-NACT MRI in predicting RR.

Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients?

Background

Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT.

Methods

This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test.

Results

A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively.

Conclusions

The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.

Magnetic Resonance Imaging Nanoprobe Quantifies Nitric Oxide for Evaluating M1/M2 Macrophage Polarization and Prognosis of Cancer Treatments

Macrophages play a crucial role in immune activation and provide great value in the prognosis of cancer treatments. Current strategies for prognostic evaluation of macrophages mainly target the specific biomarkers to reveal the number and distribution of macrophages in the tumors, whereas the phenotypic change of M1 and M2 macrophages in situ is less understood. Here, we designed an ultrasmall superparamagnetic iron oxide nanoparticle-based molecular imaging nanoprobe to quantify the repolarization of M2 to M1 macrophages by magnetic resonance imaging (MRI) using the redox-active nitric oxide (NO) as a vivid chemical target. The nanoprobe equipped with O-phenylenediamine groups could react with the intracellular NO molecules during the repolarization of M2 macrophages to the M1 phenotype, leading to electrical attraction and colloidal aggregation of the nanoprobes. Consequently, the prominent changes of the T1 and T2 relaxation in MRI allow for the quantification of the macrophage polarization. In a 4T1 breast cancer model, the MRI nanoprobe was able to reveal macrophage polarization and predict treatment efficiency in both immunotherapy and radiotherapy paradigms. This study presents a noninvasive approach to monitor the phenotypic changes of M2 to M1 macrophages in the tumors, providing insight into the prognostic evaluation of cancer treatments regarding macrophage-mediated immune responses.

A Longitudinal MRI-Based Artificial Intelligence System to Predict Pathological Complete Response After Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study

BACKGROUND

Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer.

OBJECTIVE

To develop and validate a deep learning model based on the comparison of paired MRI before and after neoadjuvant chemoradiotherapy to predict pathological complete response.

DESIGN

By capturing the changes from MRI before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation. Its performance was independently tested in an internal and 3 external validation sets, and its prognostic value was also evaluated.

SETTINGS

Multicenter study.

PATIENTS

We retrospectively enrolled 1201 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy before total mesorectal excision. Patients had been treated at 1 of 4 hospitals in China between January 2013 and December 2020.

MAIN OUTCOME MEASURES

The main outcome was the accuracy of predicting pathological complete response, measured as the area under receiver operating curve for the training and validation data sets.

RESULTS

DeepRP-RC achieved high performance in predicting pathological complete response after neoadjuvant chemoradiotherapy, with area under the curve values of 0.969 (0.942-0.996), 0.946 (0.915-0.977), 0.943 (0.888-0.998), and 0.919 (0.840-0.997) for the internal and 3 external validation sets, respectively. DeepRP-RC performed similarly well in the subgroups defined by receipt of radiotherapy, tumor location, T/N stages before and after neoadjuvant chemoradiotherapy, and age. Compared with experienced radiologists, the model showed substantially higher performance in pathological complete response prediction. The model was also highly accurate in identifying the patients with poor response. Furthermore, the model was significantly associated with disease-free survival independent of clinicopathological variables.

LIMITATIONS

This study was limited by its retrospective design and absence of multiethnic data.

CONCLUSIONS

DeepRP-RC could be an accurate preoperative tool for pathological complete response prediction in rectal cancer after neoadjuvant chemoradiotherapy.

UN SISTEMA DE IA BASADO EN RESONANCIA MAGNTICA LONGITUDINAL PARA PREDECIR LA RESPUESTA PATOLGICA COMPLETA DESPUS DE LA TERAPIA NEOADYUVANTE EN EL CNCER DE RECTO UN ESTUDIO DE VALIDACIN MULTICNTRICO

ANTECEDENTES:La predicción precisa de la respuesta a la quimiorradioterapia neoadyuvante es fundamental para las decisiones de tratamiento posteriores para los pacientes con cáncer de recto localmente avanzado.OBJETIVO:Desarrollar y validar un modelo de aprendizaje profundo basado en la comparación de resonancias magnéticas pareadas antes y después de la quimiorradioterapia neoadyuvante para predecir la respuesta patológica completa.DISEÑO:Al capturar los cambios de las imágenes de resonancia magnética antes y después de la quimiorradioterapia neoadyuvante en 638 pacientes, entrenamos un modelo de aprendizaje profundo multitarea para la predicción de respuesta (DeepRP-RC) que también permitió la segmentación simultánea. Su rendimiento se probó de forma independiente en un conjunto de validación interna y tres externas, y también se evaluó su valor pronóstico.ESCENARIO:Estudio multicéntrico.PACIENTES:Volvimos a incluir retrospectivamente a 1201 pacientes diagnosticados con cáncer de recto localmente avanzado y sometidos a quimiorradioterapia neoadyuvante antes de la escisión total del mesorrecto. Eran de cuatro hospitales en China en el período entre enero de 2013 y diciembre de 2020.PRINCIPALES MEDIDAS DE RESULTADO:Los principales resultados fueron la precisión de la predicción de la respuesta patológica completa, medida como el área bajo la curva operativa del receptor para los conjuntos de datos de entrenamiento y validación.RESULTADOS:DeepRP-RC logró un alto rendimiento en la predicción de la respuesta patológica completa después de la quimiorradioterapia neoadyuvante, con valores de área bajo la curva de 0,969 (0,942-0,996), 0,946 (0,915-0,977), 0,943 (0,888-0,998), y 0,919 (0,840-0,997) para los conjuntos de validación interna y las tres externas, respectivamente. DeepRP-RC se desempeñó de manera similar en los subgrupos definidos por la recepción de radioterapia, la ubicación del tumor, los estadios T/N antes y después de la quimiorradioterapia neoadyuvante y la edad. En comparación con los radiólogos experimentados, el modelo mostró un rendimiento sustancialmente mayor en la predicción de la respuesta patológica completa. El modelo también fue muy preciso en la identificación de los pacientes con mala respuesta. Además, el modelo se asoció significativamente con la supervivencia libre de enfermedad independientemente de las variables clinicopatológicas.LIMITACIONES:Este estudio estuvo limitado por el diseño retrospectivo y la ausencia de datos multiétnicos.CONCLUSIONES:DeepRP-RC podría servir como una herramienta preoperatoria precisa para la predicción de la respuesta patológica completa en el cáncer de recto después de la quimiorradioterapia neoadyuvante. (Traducción-Dr. Felipe Bellolio ).

Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer

BACKGROUND

To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI).

METHODS

In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87).

CONCLUSIONS

Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.

Diffusion and Perfusion Imaging in Rectal Cancer Restaging

The assessment of tumor response, after neoadjuvant radiochemotherapy (n-CRT), permits the stratification of patients for the proper therapeutical management. Although histopathology analysis of the surgical speciemen is considered the gold standard for assessing tumor response, magnetic resonance imaging (MRI), with its significant developments in technical imaging, have allowed an increase in accuracy for the evaluation of response. MRI provides a radiological tumor regression grade (mrTRG) that is correlated with the pathologic tumor regression grade (pTRG). Functional MRI parameters have additional impending in early prediction of the efficacy of therapy. Some of functional methodologies are already part of clinical practice: diffusion-weighted MRI (DW-MRI) and perfusion imaging (dynamic contrast enhanced MRI [DCE-MRI]).

Deep learning based on co-registered ultrasound and photoacoustic imaging improves the assessment of rectal cancer treatment response

Identifying complete response (CR) after rectal cancer preoperative treatment is critical to deciding subsequent management. Imaging techniques, including endorectal ultrasound and MRI, have been investigated but have low negative predictive values. By imaging post-treatment vascular normalization using photoacoustic microscopy, we hypothesize that co-registered ultrasound and photoacoustic imaging will better identify complete responders. In this study, we used in vivo data from 21 patients to develop a robust deep learning model (US-PAM DenseNet) based on co-registered dual-modality ultrasound (US) and photoacoustic microscopy (PAM) images and individualized normal reference images. We tested the model's accuracy in differentiating malignant from non-cancer tissue. Compared to models based on US alone (classification accuracy 82.9 ± 1.3%, AUC 0.917(95%CI: 0.897-0.937)), the addition of PAM and normal reference images improved the model performance significantly (accuracy 92.4 ± 0.6%, AUC 0.968(95%CI: 0.960-0.976)) without increasing model complexity. Additionally, while US models could not reliably differentiate images of cancer from those of normalized tissue with complete treatment response, US-PAM DenseNet made accurate predictions from these images. For use in the clinical settings, US-PAM DenseNet was extended to classify entire US-PAM B-scans through sequential ROI classification. Finally, to help focus surgical evaluation in real time, we computed attention heat maps from the model predictions to highlight suspicious cancer regions. We conclude that US-PAM DenseNet could improve the clinical care of rectal cancer patients by identifying complete responders with higher accuracy than current imaging techniques.

Diffusion kurtosis imaging and MRI-detected extramural venous invasion in rectal cancer: correlation with clinicopathological prognostic factors

OBJECTIVE

To investigate the prognostic value of the diffusion kurtosis imaging (DKI)-derived parameters D value, K value, diffusion-weighted imaging (DWI) parameter apparent diffusion coefficient (ADC) value, and magnetic resonance imaging (MRI)-detected extramural venous invasion (EMVI) (mrEMVI) in rectal cancer patients.

METHODS

Forty patients who underwent MRI for rectal cancer were retrospectively evaluated. DKI-derived parameters D and K were measured using the Medical Imaging Interaction Toolkit. Conventional ADC values were measured from the corresponding DWI images. An experienced radiologist evaluated the mrEMVI status on MR images using the mrEMVI scoring system. An independent sample t-test or analysis of variance was used to analyze and compare the measurement data. The x² test or Fisher exact test was used for categorical variables. Receiver operating characteristic curves were used to assess the diagnostic performance of these parameters.

RESULTS

Among the 40 patients, MRI showed positive EMVI in 15 patients and negative EMVI in 25 patients. Positive mrEMVI status was associated with age, positive circumferential resection margin, pT-stage, lymphovascular invasion (LVI), distant metastasis, and serum carcinoembryonic antigen (CEA) level (P = 0.004-0.036). The dispersion coefficient (D) values and ADC values were significantly higher in the mucinous adenocarcinoma (MC) group than in the common adenocarcinoma (AC) group (P = 0.001), while kurtosis coefficient (K) values were lower in the MC group than in the AC group (P = 0.022). D values were significantly higher in the KRAS-mutated group than in the wild-type group (P < 0.05), whereas K values were lower in the KRAS-mutated group than in the wild-type group (P < 0.05). All three parameters (D, K, and ADC values) showed good diagnostic performance for discriminating MC from AC. Both the D and K values showed certain diagnostic performance for discriminating KRAS mutation.

CONCLUSION

DKI-derived parameters, conventional ADC values, and mrEMVI are associated with different histopathological prognostic factors. All DKI-derived parameters and conventional ADC values may distinguish MC from AC. DKI-derived parameters may also be used to discriminate KRAS mutation.

Determinants of Pre-Surgical Treatment in Primary Rectal Cancer: A Population-Based Study

When preoperative radiotherapy (RT) is best used in rectal cancer is subject to discussions and guidelines differ. To understand the selection mechanisms, we analysed treatment decisions in all patients diagnosed between 2010-2020 in two Swedish regions (Uppsala with a RT department and Dalarna without). Information on staging and treatment (direct surgery, short-course RT, or combinations of RT/chemotherapy) in the Swedish Colorectal Cancer Registry were used. Staging magnetic resonance imaging (MRI) permitted a division into risk groups, according to national guidelines. Logistic regression explored associations between baseline characteristics and treatment, while Cohen's kappa tested congruence between clinical and pathologic stages. A total of 1150 patients without synchronous metastases were analysed. Patients from Dalarna were older, had less advanced tumours and were pre-treated less often (52% vs. 63%, p < 0.001). All MRI characteristics (T-/N-stage, MRF, EMVI) and tumour levels were important for treatment choice. Age affected if chemotherapy was added. The correlation between clinical and pathological T-stage was fair/moderate and poor for N-stage. The MRI-based risk grouping influenced treatment choice the most. Since the risk grouping was modified to diminish the pre-treated proportion, fewer patients were irradiated with time. MRI staging is far from optimal. A stronger wish to decrease irradiation may explain why fewer patients from Dalarna were irradiated, but inequality in health care cannot be ruled out.

A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer

PURPOSE

The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC).

METHODS

A total of 162 patients with RC were included in the study. The MRI reported model, the Radscore model, and the Complex model were constructed using the logistics regression (LR) algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of these models. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration.

RESULT

All three models constructed by the LR algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the Complex model outperformed the MRI reported model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the Complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI) 0.848-0.957) in the training group and an AUC of 0.891 (95% CI 0.799-0.983) in the validation group. Meanwhile, the nomogram showed good calibration.

CONCLUSION

The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.

Active surveillance in long period of total neoadjuvant therapy in rectal cancer: Early prediction of poor regression response

AIM

To analyze locally advanced rectal cancer (LARC) patients and tumor characteristics during the period of total neoadjuvant therapy (TNT) and explore the risk factors that may predict poor tumor regression in response to TNT.

MATERIALS AND METHODS

The data of 120 LARC patients who received TNT from December 2016 and September 2019 in our hospital were retrospectively analyzed. The clinicopathological characteristics of patients with different tumor regression responses were compared. Then we divided patients into two groups according to the carcinoembryonic antigen (CEA) clearance pattern after chemoradiation to explore risk factors that might predict the tumor regression response.

RESULTS

Of 120 LARC patients, 34 (28.3%) exhibited poor regression. Stratified analysis by tumor response showed that patients with poor response to TNT were more likely to obtain elevated CEA during the course of TNT (all P < 0.05). For those with elevated pretreatment CEA, fewer patients with poor response obtained normal CEA after chemoradiation (13.6% vs. 72.7%, P < 0.001). Besides, less patients' CEA levels in the poor response group decreased by greater than 50% after chemoradiation when compared with that in the good response group (18.2% vs. 60.6%, P = 0.002). Stratified analysis by CEA clearance pattern after chemoradiation showed patients who obtained an elevated pretreatment CEA and decreased by less than 50% after chemoradiation were more likely to have poor response to TNT compared to others (76.2% vs. 18.2%, P < 0.001). Logistic multivariate analysis revealed that cN2 (95% CI 1.553-16.448), larger tumors (95% CI 2.250-21.428) and CEA clearance pattern after chemoradiation (95% CI 1.062-66.992) were independent risk factors for poor tumor regression response.

CONCLUSION

Approximately one-fourth of LARC patients with TNT achieved a poor regression response. Here, cN2, larger tumor size before treatment and elevated CEA levels were considered predictive features of a poor response. Active surveillance of CEA levels during the TNT course are potentially important, and CEA levels after chemoradiation might have important implications for the tumor response to TNT.

ADC Values of Cytologically Benign and Cytologically Malignant 18 F-FDG PET-Positive Lymph Nodes of Head and Neck Squamous Cell Carcinoma

Simple Summary

In squamous cell carcinoma of the head and neck, 18F-fluordeoxyglucose positron emission tomography (FDG-PET), diffusion-weighted magnetic resonance imaging (DW-MRI) and ultrasound-guided fine needle aspiration are commonly used imaging tools for nodal staging (N-staging). Although FDG-PET has good performance in nodal detection, it is still difficult to distinguish between PET-positive reactive and malignant nodes for the purpose of selecting nodes to be aspirated. DW-MRI can help to detect small lymph node metastases, and an inverse correlation with FDG uptake is expected. We found a mild negative correlation between SUVmax and ADC. Comparing the apparent diffusion coefficient (ADC) values between PET-positive and PET-negative nodes, ADC was significantly higher in PET-negative nodes. Whereas no significantly lower ADC value of cytological malignant nodes could be found overall, in the subgroup of non-HPV-related nodes, the ADC values of cytologically malignant PET-positive nodes were significantly lower than in cytologically benign nodes. This finding might be helpful in selecting nodes for puncture.

Abstract

Nodal staging (N-staging) in head and neck squamous cell carcinoma (HNSCC) is essential for treatment planning and prognosis. 18F-fluordeoxyglucose positron emission tomography (FDG-PET) has high performance for N-staging, although the distinction between cytologically malignant and reactive PET-positive nodes, and consequently, the selection of nodes for ultrasound-guided fine needle aspiration cytology (USgFNAC), is challenging. Diffusion-weighted magnetic resonance imaging (DW-MRI) can help to detect nodal metastases. We aim to investigate the potential of the apparent diffusion coefficient (ADC) as a metric to distinguish between cytologically reactive and malignant PET-positive nodes in order to improve node selection criteria for USgFNAC. PET-CT, real-time image-fused USgFNAC and DW-MRI to calculate ADC were available for 78 patients offered for routine N-staging. For 167 FDG-positive nodes, differences in the ADC between cytologically benign and malignant PET-positive nodes were evaluated, and both were compared to the ADC values of PET-negative reference nodes. Analyses were also performed in subsets of nodes regarding HPV status. A mild negative correlation between SUVmax and ADC was found. No significant differences in ADC values were observed between cytologically malignant and benign PET-positive nodes overall. Within the subset of non-HPV-related nodes, ADC_b0-200-1000 was significantly lower in cytologically malignant PET-positive nodes when compared to benign PET-positive nodes. ADC_b0-1000 and ADCb_0-200-1000 were significantly lower (p = 0.018, 0.016, resp.) in PET-negative reference nodes than in PET-positive nodes. ADC was significantly higher in PET-negative reference nodes than in PET-positive nodes. The non-HPV-related subgroup showed significantly (p = 0.03) lower ADC values in cytologically malignant than in cytologically benign PET-positive nodes, which should help inform the node selection procedure for puncture.

ctDNA for Risk of Recurrence Assessment in Patients Treated with Neoadjuvant Treatment: A Systematic Review and Meta-analysis

BACKGROUND

We wanted to investigate the association between circulating tumor DNA (ctDNA) detection at baseline, during and after neoadjuvant treatment, after surgery, and recurrence, in patients with nonmetastatic cancer.

PATIENTS AND METHODS

In this systematic review and meta-analysis, we included studies that investigated patients undergoing neoadjuvant treatment for nonmetastatic cancer and provided recurrence indices stratified for ctDNA status at the following timepoints: baseline, during treatment, posttreatment, and postsurgery. Study quality was reported with the Newcastle-Ottawa scale, REMARK checklist, and GRADE approach. PubMed, Embase, Cochrane Library, and Web of Science were our data sources (inception to 3 June 2021). The main outcome was risk of recurrence.

RESULTS

We identified ten studies including 727 patients with rectal, breast, gastric, and bladder cancer. All studies reported posttreatment ctDNA analysis, while seven, four, and six reported baseline, during treatment, and postsurgery ctDNA analysis, respectively. ctDNA detection was associated to recurrence across all timepoints [baseline: risk ratio (RR) 2.86, 95% confidence interval (CI) 1.33-6.14, during treatment: RR 3.81, 95% CI 2.09-6.92, posttreatment: RR 4.29, 95% CI 2.79-6.60, postsurgery: RR 8.03, 95% CI 3.16-20.43]. Heterogeneity was low to moderate.

CONCLUSIONS

This meta-analysis of observational studies found that ctDNA detection in patients undergoing neoadjuvant treatment for nonmetastatic cancer was associated with recurrence. A stronger association was evident in posttreatment and postsurgery timepoints. However, some studies reported low negative predictive value (NPV) of pathological complete response, showing that ctDNA-detection-guided escalation and de-escalation studies following neoadjuvant treatment regimens are needed before its role as a treatment guidance can be affirmed.

Diagnostic performance of MRI and endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy: a systematic review of the literature

BACKGROUND

The diagnostic performance of magnetic resonance imaging (MRI) modalities and/or endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy (nCRT) is unclear.

PURPOSE

To summarize existing evidence on the diagnostic performance of diffusion-weighted MRI, perfusion-weighted MRI, T2-weighted MR tumor regression grade, and/or endoscopy for assessing complete tumor response after nCRT.

MATERIAL AND METHODS

MEDLINE and Embase databases were searched. The PRISMA guidelines were followed. Sensitivity, specificity, negative predictive, and positive predictive values were retrieved from included studies.

RESULTS

In total, 81 studies were eligible for inclusion. Evidence suggests that combined use of MRI and endoscopy tends to improve the diagnostic performance compared to single imaging modality. The positive predictive value of a complete response varies substantially between studies. There is considerable heterogeneity between studies.

CONCLUSION

Combined re-staging tends to improve diagnostic performance compared to single imaging modality, but the vast majority of studies fail to offer true clinical value due to the study heterogeneity.

The efficiency of 18F-FDG-PET/CT in the assessment of tumor response to preoperative chemoradiation therapy for locally recurrent rectal cancer

Background

Locally recurrent rectal cancer (LRRC) remains a major problem after curative resection of primary rectal cancer. A noninvasive, prognostic biomarker with which to accurately evaluate disease status and assess the treatment response is critically needed to optimize treatment plans. This study assesses the effectiveness of PET/CT evaluation of preoperative chemoradiation therapy (CRT) in patients with LRRC.

Methods

Since 2004, we have been performing preoperative CRT to improve local tumor control and survival. Between 2004 and 2013, 40 patients with LRRC underwent preoperative CRT (radiation: 50 Gy/25 fractions; chemotherapy: irinotecan plus UFT [tegafur and uracil]/leucovorin) and radical surgery, and underwent ¹⁸F-FDG-PET/CT before and 3 weeks after the completion of CRT. The maximum standardized uptake values (SUVmax) of the pre-CRT scan (Pre-SUV) and the post-CRT scan (Post-SUV) were measured. The predictive value of the ¹⁸F-FDG-PET and CT/MRI response assessments was evaluated.

Results

The mean Pre-SUV was significantly higher than the Post-SUV (8.2 ± 6.1, vs. 3.8 ± 4.0; P < 0.0001). Following CRT, 17/40 patients (42.5%) were classified as responders according to the Mandard tumor regression grade (TRG1–2). The mean Post-SUV was significantly lower in responders than in nonresponders (2.0 ± 1.7 vs. 5.1 ± 3.9; P = 0.0038). Pathological response was not correlated with the response as evaluated by CT (P > 0.9999) or MRI (P > 0.9999). Multivariate regression analysis identified Post-SUV as an independent predictor of local re-recurrence-free survival (P = 0.0383) and for overall survival (P = 0.0195).

Conclusions

PET/CT is useful in assessing tumor response to preoperative CRT for LRRC and predicting prognosis after surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08873-7.

Validation of the standardized index of shape tool to analyze DCE-MRI data in the assessment of neo-adjuvant therapy in locally advanced rectal cancer

PURPOSE

Standardized index of shape (SIS) tool validation to examine dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in preoperative chemo-radiation therapy (pCRT) assessment of locally advanced rectal cancer (LARC) in order to guide the surgeon versus more or less conservative treatment.

MATERIALS AND METHODS

A total of 194 patients (January 2008-November 2020), with III-IV locally advanced rectal cancer and subjected to pCRT were included. Three expert radiologists performed DCE-MRI analysis using SIS tool. Degree of absolute agreement among measurements, degree of consistency among measurements, degree of reliability and level of variability were calculated. Patients with a pathological tumour regression grade (TRG) 1 or 2 were classified as major responders (complete responders have TRG 1).

RESULTS

Good significant correlation was obtained between SIS measurements (range 0.97-0.99). The degree of absolute agreement ranges from 0.93 to 0.99, the degree of consistency from 0.81 to 0.9 and the reliability from 0.98 to 1.00 (p value <  < 0.001). The variability coefficient ranges from 3.5% to 26%. SIS value obtained to discriminate responders by non-responders a sensitivity of 95.9%, a specificity of 84.7% and an accuracy of 91.8% while to detect complete responders, a sensitivity of 99.2%, a specificity of 63.9% and an accuracy of 86.1%.

CONCLUSION

SIS tool is suitable to assess pCRT response both to identify major responders and complete responders in order to guide the surgeon versus more or less conservative treatment.

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10^–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Delayed Surgery after Neoadjuvant Treatment for Rectal Cancer Does Not Lead to Impaired Quality of Life, Worry for Cancer, or Regret

Non operative management of complete clinical responders after neoadjuvant treatment for rectal cancer enjoys an increasing popularity because of the increased functional outcome results. Even a near complete response can evolve in a cCR, and therefore further delaying response assessment is accepted. However, up to 40% of patients will develop a regrowth and will eventually require delayed surgery. It is presently unknown if and to what extent quality of life of these patients is affected, compared to patients who undergo immediate surgery. Between January 2015-May 2020, 200 patients were treated with neoadjuvant therapy of whom 94 received TME surgery. Fifty-one (59%) of 87 alive patients returned the questionnaires: 33 patients who underwent immediate and 18 patients who underwent delayed surgery. Quality of life was measured through the QLQ-C30, QLQ-CR29, and Cancer Worry Scale questionnaires. Regret to participate in repeated response assessment protocol was assessed through the Decision Regret Scale. Exploratory factor analysis (EFA) and a 'known groups comparison' was performed to assess QLQ questionnaires validity in this sample. Higher mean physical function scores (89.2 vs. 77.6, p = 0.03) were observed in the immediate surgery group, which lost significance after correction for operation type (p = 0.25). Arousal for men was higher in the delayed surgery group (20.0 vs. 57.1, p = 0.02). There were no differences between surgical groups for the other questionnaire items. Worry for cancer was lower in the delayed surgery group (10.8 vs. 14.0, p = 0.21). Regret was very low (12-16%). EFA reproduced most QLQ C-30 and CR29 subscales with good internal consistency. Quality of life is not impaired in patients undergoing delayed TME surgery after neoadjuvant treatment for rectal cancer. Moreover, there is very low regret and no increase in worry for cancer. Therefore, from a quality of life perspective, this study supports a repeated response assessment strategy after CRTx for rectal carcinoma to identify all complete responders.

Staging of Locally Advanced Rectal Cancer Beyond TME

The management of rectal cancer is complex and continually evolving. With advancements in technology and the use of multidisciplinary teams to guide the treatment decision making, staging, oncologic, and functional outcomes are improving, and the management is moving toward personalized treatment strategies to optimize each individual patient's outcomes. Key in this evolution is imaging. Magnetic resonance imaging (MRI) has emerged as the dominant method of pelvic imaging in rectal cancer, and use of MRI for staging is best practice in multiple international guidelines. MRI allows a noninvasive assessment of the tumor site, relationship to surrounding structures, and provides highly accurate rectal cancer staging, which is necessary for determining the appropriate treatment strategy. However, the applications of MRI extend far beyond pretreatment staging. MRI can be used to predict outcomes in locally advanced rectal cancer and guide the surgical or nonsurgical plan, serving as a predictive and prognostic biomarker. With continued MRI hardware improvement and new sequence development, MRI may offer new perspectives in the assessment of treatment response and new innovations that could provide better insight into the staging, restaging, and outcomes with rectal cancer.

The multidisciplinary management of rectal cancer

Rectal cancer treatment has evolved during the past 40 years with the use of a standardized surgical technique for tumour resection: total mesorectal excision. A dramatic reduction in local recurrence rates and improved survival outcomes have been achieved as consequences of a better understanding of the surgical oncology of rectal cancer, and the advent of adjuvant and neoadjuvant treatments to compliment surgery have paved the way for a multidisciplinary approach to disease management. Further improvements in imaging techniques and the ability to identify prognostic factors such as tumour regression, extramural venous invasion and threatened margins have introduced the concept of decision-making based on preoperative staging information. Modern treatment strategies are underpinned by accurate high-resolution imaging guiding both neoadjuvant therapy and precision surgery, followed by meticulous pathological scrutiny identifying the important prognostic factors for adjuvant chemotherapy. Included in these strategies are organ-sparing approaches and watch-and-wait strategies in selected patients. These pathways rely on the close working of interlinked disciplines within a multidisciplinary team. Such multidisciplinary forums are becoming standard in the treatment of rectal cancer across the UK, Europe and, more recently, the USA. This Review examines the essential components of modern-day management of rectal cancer through a multidisciplinary team approach, providing information that is essential for any practising colorectal surgeon to guide the best patient care.

Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters

PURPOSE

To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI).

MATERIALS AND METHODS

Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (D_t), pseudo-diffusion (D_p), perfusion fraction (f_p)] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed.

RESULTS

Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM D_p and mean value of VARPRO D_p pre-treatment showed statistically significant differences to predict pCR. (p value at Mann-Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO D_p mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment).

CONCLUSION

SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO F_p mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.

"Watch and Wait" for complete clinical response after neoadjuvant chemoradiotherapy for rectal cancer

The management of rectal cancer has evolved substantially over recent decades, becoming increasingly complex. This was once a disease associated with high mortality and limited treatment options that typically necessitated a permanent colostomy, has now become a model for multidisciplinary evaluation, treatment and surgical advancement. Despite advances in the rates of total mesorectal excision, decreased local recurrence and increased 5-year survival rates, the multimodal treatment of rectal cancer is associated with a significant impact on long-term functional and quality of life outcomes including risks of bowel, bladder and sexual dysfunction, and potential need for a permanent stoma. There is great interest in strategies to decrease the toxicity of treatment, including selective use of radiation, chemotherapy or even surgery. The modern concept of selective use of surgery for patients with rectal cancer are based on the observed pathological complete response in approximately 10-20% of patients following long-course chemoradiation therapy. While definitive surgical resection remains the standard of care for all patients with non-metastatic rectal cancer, a growing number of studies are providing supportive evidence for a watch-and-wait, organ preserving approach in highly selected patients with rectal cancer. However, questions regarding the heterogeneity of patient selection, optimal method for inducing pathological complete response, methods and intervals for assessing treatment response and adequacy of follow-up remain unanswered. The aim of this review is to provide an up-to-date summary of the current evidence for the watch-and-wait management of rectal cancer following a complete clinical response after neoadjuvant chemoradiation.

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHODS

Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR.

RESULTS

The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower Correlat_D value. Higher Correlat_D*, Correlat_f, SumAverg_ADC, and SumAverg_D values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, Correlat_D* (P < 0.001), DifVarnc_ADC (P = 0.024), and DifVarnc_D (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%.

CONCLUSION

Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.

Accurate outcome prediction after neo-adjuvant radio-chemotherapy for rectal cancer based on a TCP-based early regression index

•

A TCP-based early regression index (ERI_TCP) was previously introduced.

•

ERI_TCP was associated to improved survival after neo-adjuvant therapy for rectal cancer.

•

Distant-metastasis-free survival was predicted by ERI_TCP and 5-FU dose.

•

The resulting AUC (0.86) was significantly higher than models not including T ERI_TCP.

•

ERI_TCP is a promising tool for therapy personalization.

Background and purpose

An early tumor regression index (ERI_TCP) was previously introduced and found to predict pathological response after neo-adjuvant radio-chemotherapy of rectal cancer. ERI_TCP was tested as a potential biomarker in predicting long-term disease-free survival.

Materials and methods

Data of 65 patients treated with an early regression-guided adaptive boosting technique (ART) were available. Overall, loco-regional relapse-free and distant metastasis-free survival (OS, LRFS, DMFS) were considered. Patients received 41.4 Gy in 18 fractions (2.3 Gy/fr), including ART concomitant boost on the residual GTV during the last 6 fractions (3 Gy/fr, D_mean: 45.6 Gy). Chemotherapy included oxaliplatin and 5-fluorouracil (5-FU). T2-weighted MRI taken before (MRI_pre) and at half therapy (MRI_half) were available and GTVs were contoured (V_pre, V_half). The parameter ERI_TCP = −ln[(1 − (V_half/V_pre))^Vpre] was calculated for all patients. Cox regression models were assessed considering several clinical and histological variables. Cox models not including/including ERI_TCP (CONV_model and REGR_model respectively) were assessed and their discriminative power compared.

Results

At a median follow-up of 47 months, OS, LRFS and DMFS were 94%, 95% and 78%. Due to too few events, multivariable analyses focused on DMFS: the resulting CONV_model included pathological complete remission or clinical complete remission followed by surgery refusal (HR: 0.15, p = 0.07) and 5-FU dose >90% (HR: 0.29, p = 0.03) as best predictors, with AUC = 0.75. REGR_model included ERI_TCP (HR: 1.019, p < 0.0001) and 5-FU dose >90% (HR: 0.18, p = 0.005); AUC was 0.86, significantly higher than CONV_model (p = 0.05). Stratifying patients according to the best cut-off value for ERI_TCP and to 5-FU dose (> vs <90%) resulted in 47-month DMFS equal to 100%/69%/0% for patients with two/one/zero positive factors respectively (p = 0.0002). ERI_TCP was also the only variable significantly associated to OS (p = 0.01) and LRFS (p = 0.03).

Conclusion

ERI_TCP predicts long-term DMFS after radio-chemotherapy for rectal cancer: an independent impact of the 5-FU dose was also found. This result represents a first step toward application of ERI_TCP in treatment personalization: additional confirmation on independent cohorts is warranted.

Value of High-Resolution DWI in Combination With Texture Analysis for the Evaluation of Tumor Response After Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer

OBJECTIVE. The purpose of this study is to determine the performance of the apparent diffusion coefficient (ADC) value calculated from high-resolution DWI using readout-segmented echo-planar imaging (rs-EPI) and to assess the texture parameters of T2-weighted MR images in identifying pathologic complete response (pCR) after patients with locally advanced rectal cancer (LARC) undergo preoperative chemoradiotherapy (CRT). MATERIALS AND METHODS. A total of 76 patients with LARC who underwent preoperative CRT and subsequent surgery were enrolled in the study retrospectively. All patients underwent post-CRT MRI, which included acquisition of a DWI sequence with use of the rs-EPI technique. The histopathologic tumor regression grade was the reference standard. Patients were subdivided into pCR and non-pCR groups. Two radiologists independently drew whole-tumor ROIs on DW images and T2-weighted MR images to calculate the mean ADC value and first-order texture parameters. RESULTS. Interobserver agreement was good to excellent (intraclass correlation coefficient [ICC], 0.79-0.993) for imaging analysis. Calculated from high-resolution DWI, the mean post-CRT ADC value was significantly higher in the pCR group (p < 0.001). The pCR group also showed lower uniformity (p < 0.001) of the T2-weighted image. The mean ADC value and uniformity were significantly correlated with the tumor regression grade. The mean ADC value was a good indicator for differentiating pCR from absence of pCR (ROC AUC value, 0.912). Uniformity (ROC AUC value, 0.776) showed a moderate ability to identify pCR. Combining the mean ADC value and uniformity yielded an ROC AUC value comparable to that of the mean ADC value (p = 0.125). CONCLUSION. Mean post-CRT ADC values calculated from high-resolution DWI using rs-EPI could effectively select for patients with LARC who have a pCR after preoperative CRT. First-order texture parameters of T2-weighted MR images could also identify patients with pCR by reflecting tumor heterogeneity, even though they could not significantly improve the diagnostic performance.

Assessing response to neo-adjuvant therapy in locally advanced rectal cancer using Intra-voxel Incoherent Motion modelling by DWI data and Standardized Index of Shape from DCE-MRI

Background:

Our aim was to investigate preoperative chemoradiation therapy (pCRT) response in locally advanced rectal cancer (LARC) comparing standardized index of shape (SIS) obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with intravoxel-incoherent-motion-modelling-derived parameters by diffusion-weighted imaging (DWI).

Materials and methods:

Eighty-eight patients with LARC were subjected to MRI before and after pCRT. Apparent diffusion coefficient (ADC), tissue diffusion (D_t), pseudodiffusion (D_p) and perfusion fraction (f) were calculated and percentage changes ∆ADC, ∆D_t, ∆D_p, ∆f were computed. SIS was derived comparing DCE-MRI pre- and post-pCRT. Nonparametric tests and receiver operating characteristic (ROC) curves were performed.

Results:

A total of 52 patients were classified as responders (tumour regression grade; TRG ⩽ 2) and 36 as not-responders (TRG > 3). Mann–Whitney U test showed statistically significant differences in SIS, ∆ADC and ∆D_t between responders and not-responders and between complete responders (19 patients with TRG = 1) versus incomplete responders. The best parameters to discriminate responders by nonresponders were SIS and ∆ADC, with an accuracy of 91% and 82% (cutoffs of −5.2% and 18.7%, respectively); the best parameters to detect pathological complete responders were SIS, ∆f and ∆D_p with an accuracy of 78% (cutoffs of 38.5%, 60.0% and 83.0%, respectively). No increase of performance was observed by combining linearly each possible couple of parameters or combining all parameters.

Conclusion:

SIS allows assessment of preoperative treatment response with high accuracy guiding the surgeon versus more or less conservative treatment. DWI-derived parameters reached less accuracy compared with SIS and combining linearly DCE- and DWI-derived parameters; no increase of accuracy was obtained.

Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI

BACKGROUND

Tumor heterogeneity can be assessed by texture analysis (TA). TA has been applied using diffusion-weighted imaging and apparent diffusion coefficient maps to predict pathological responses to preoperative chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC).

PURPOSE

To evaluate the texture parameters obtained from K ^trans maps derived from dynamic contrast-enhanced (DCE)-MRI for predicting pathological responses to preoperative CRT for LARCs.

STUDY TYPE

Retrospective.

POPULATION

Altogether, 83 patients (26 women, 57 men) with rectal cancer met the inclusion criteria.

FIELD STRENGTH/SEQUENCE

3.0T/T₁ -weighted DCE-MRI sequence.

ASSESSMENT

After CRT, each tumor was assessed by a pathologist who assigned a tumor regression grade (TRG), thereby identifying pathologically complete responders (pCR; TRG 1) and good responders (GR; TRG1 + TRG2). TA was then applied to the DCE-MRI K ^trans maps. The K ^trans value, several TA parameters, and tumor volumes were calculated.

STATISTICAL TESTS

The Shapiro-Wilk test was used to verify that the data had normal distribution. Results of parameters measured before and after CRT were compared using paired-sample t-tests. Value changes of each parameter in the combined pCR/GR group were compared using independent sample t-tests. Receiver operating characteristic curves and areas under the curve (AUC) were calculated to assess the diagnostic performance of each parameter related to CRT effectiveness.

RESULTS

There were 15 pCR (16.9%) and 21 GR (25.3%) patients. Tumor volume, mean K ^trans , entropy, and correlation decreased and energy values increased significantly in these groups compared with those of the non-PCR and non-GR groups. ΔCorrelation (Δcorrelation = postcorrelation - precorrelation) was found to be a valuable parameter for identifying pCR/GR patients (AUC 0.895, sensitivity 86.7%, specificity 81.8%).

DATA CONCLUSION

TA parameters from the DCE-MRI K ^trans map can predict the efficacy of CRT for treating LARCs. Also, Δcorrelation may be useful for identifying patients who will be responsive to CRT.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:885-893.

Can clinicopathological parameters predict for lymph node metastases in ypT0-2 rectal carcinoma? Results of the CAO/ARO/AIO-94 and CAO/ARO/AIO-04 phase 3 trials

BACKGROUND

The advent of less radical surgical approaches has generated concern about leaving locoregional lymph node metastases (LNM) unresected that could lead to adverse outcome. We examined the prognostic role of clinicopathological factors for ypN-positivity in patients with ypT0-2 rectal carcinoma treated within the CAO/ARO/AIO-94 and CAO/ARO/AIO-04 randomized phase 3 trials.

METHODS

The correlation of clinicopathological factors with ypN-status (ypN0 vs ypN1/2) was examined in n = 776 patients with ypT0-2 rectal carcinoma after preoperative CRT and total mesorectal excision surgery using Pearson's Chi-squared test for categorical variables and Kruskal-Wallis' test for continuous variables. Multivariable analysis was performed using binary logistic regression to identify independent prognosticators for ypN-positivity.

RESULTS

Residual LNM (ypN+) were found in 6%, 20.8% and 21.4% of patients with ypT0, ypT1 and ypT2 carcinomas, respectively. Independent prognosticators for LNM were advanced ypT category (p = 0.002) and lymphatic invasion (p = 0.020). In a separate multivariable analysis performed upon exclusion of ypT-category due to multicollinearity with residual tumor diameter (RTD), lymphatic invasion (p = 0.015) and RTD ≥10 mm (p = 0.005) demonstrated strong correlation with LNM.

CONCLUSION

Advanced ypT-stage, lymphatic invasion and RTD ≥10 mm were prognostic factors for LNM in patients ypT0-2 rectal carcinoma treated with CRT and surgery within both phase 3 trials. The high incidence of LNM in the ypT1-2 group needs to be taken into consideration in the context of oncological safety and indicate that LE should be advocated with great caution in this patient subgroup. The prognostic pathological factor identified here could help guide decision of LE vs TME after standard CRT.

Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma

Background

To investigate the capability of radiomic analysis using T2-weighted (T2W) and spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI) for predicting the therapeutic response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT).

Methods

Pretreatment T2W- and SPAIR T2W-MRI of 68 ESCC patients (37 responders, 31 nonresponders) were analyzed. A number of 138 radiomic features were extracted from each image sequence respectively. Kruskal-Wallis test were performed to evaluate the capability of each feature on treatment response classification. Sensitivity and specificity for each of the studied features were derived using receiver operating characteristic (ROC) analysis. Support vector machine (SVM) and artificial neural network (ANN) models were constructed based on the training set (23 responders, 20 nonresponders) for the prediction of treatment response, and then the testing set (14 responders, 11 nonresponders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test.

Results

Radiomic analysis showed significance in the prediction of treatment response. The analyses showed that complete responses (CRs) versus stable diseases (SDs), partial responses (PRs) versus SDs, and responders (CRs and PRs) versus nonresponders (SDs) could be differentiated by 26, 17, and 33 features (T2W: 11/11/15, SPAIR T2W: 15/6/18), respectively. The prediction models (ANN and SVM) based on features extracted from SPAIR T2W sequence (SVM: 0.929, ANN: 0.883) showed higher accuracy than those derived from T2W (SVM: 0.893, ANN: 0.861). No statistical difference was observed in the performance of the two classifiers (P=0.999).

Conclusions

Radiomic analysis based on pretreatment T2W- and SPAIR T2W-MRI can be served as imaging biomarkers to predict treatment response to CRT in ESCC patients.

Standardized Index of Shape (DCE-MRI) and Standardized Uptake Value (PET/CT): Two quantitative approaches to discriminate chemo-radiotherapy locally advanced rectal cancer responders under a functional profile

Purpose

To investigate dynamic contrast enhanced-MRI (DCE-MRI) in the preoperative chemo-radiotherapy (CRT) assessment for locally advanced rectal cancer (LARC) compared to¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT).

Methods

75 consecutive patients with LARC were enrolled in a prospective study. DCE-MRI analysis was performed measuring SIS: linear combination of percentage change (Δ) of maximum signal difference (MSD) and wash-out slope (WOS). ¹⁸F-FDG PET/CT analysis was performed using SUV maximum (SUV_max). Tumor regression grade (TRG) were estimated after surgery. Non-parametric tests, receiver operating characteristic were evaluated.

Results

55 patients (TRG1-2) were classified as responders while 20 subjects as non responders. ΔSIS reached sensitivity of 93%, specificity of 80% and accuracy of 89% (cut-off 6%) to differentiate responders by non responders, sensitivity of 93%, specificity of 69% and accuracy of 79% (cut-off 30%) to identify pathological complete response (pCR). Therapy assessment via ΔSUV_max reached sensitivity of 67%, specificity of 75% and accuracy of 70% (cut-off 60%) to differentiate responders by non responders and sensitivity of 80%, specificity of 31% and accuracy of 51% (cut-off 44%) to identify pCR.

Conclusions

CRT response assessment by DCE-MRI analysis shows a higher predictive ability than ¹⁸F-FDG PET/CT in LARC patients allowing to better discriminate significant and pCR.

Predictive role of microRNA-related genetic polymorphisms in the pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients

In rectal cancer, a pathologic complete response (pCR) to pre-operative treatment is a favourable prognostic marker, but is reported in a minority of the patients. We aimed at identifying microRNA-related host genetic polymorphisms predictive of pCR.

A panel of 114 microRNA-related tagging polymorphisms was selected and analyzed on 265 locally advanced rectal cancer patients treated with neoadjuvant chemo-radiotherapy. Patients were stratified in two subgroups according to the radiotherapy dose (50.4Gy for 202 patients, 55.0Gy for 78 patients). Interactions among genetic and clinical-pathological variants were investigated by recursive partitioning analysis.

Only polymorphisms with a consistent significant effect in the two subgroups of patients were selected as predictive markers of pCR. The results were validated by bootstrap analysis. SMAD3-rs744910, SMAD3-rs745103, and TRBP-rs6088619 were associated to an increased chance of pCR (p=0.0153, p=0.0471, p=0.0125). DROSHA-rs10719 and SMAD3-rs17228212 had an opposite detrimental effect on pathological tumour response (p=0.0274, p=0.0049). Recursive partitioning analysis highlighted that a longer interval time between the end of radiotherapy and surgery increases the chance of pCR in patients with a specific combination of SMAD3-rs744910 and TRBP-rs6088619 genotypes.

This study demonstrated that microRNA-related host genetic polymorphisms can predict pCR to neo-adjuvant chemo-radiotherapy, and could be used to personalize the interval time between the end of radiotherapy and surgery.

Diffusion-weighted magnetic resonance imaging of rectal cancer: tumour volume and perfusion fraction predict chemoradiotherapy response and survival

BACKGROUND

In locally advanced rectal cancer (LARC), responses to preoperative treatment are highly heterogeneous and more accurate diagnostics are likely to enable more individualised treatment approaches with improved responses. We investigated the potential of diffusion-weighted magnetic resonance imaging (DW MRI), with quantification of the apparent diffusion coefficient (ADC) and perfusion fraction (F), as well as volumetry from T2-weighted (T2W) MRI, for prediction of therapeutic outcome.

MATERIAL AND METHODS

In 27 LARC patients receiving neoadjuvant chemotherapy (NACT) before chemoradiotherapy (CRT), T2W- and DW MRI were obtained before and after NACT. Tumour volumes were delineated in T2W MRI and ADCs and Fs were estimated from DW MRI using a simplified approach to the intravoxel incoherent motion (IVIM) model. Mean tumour values and histogram analysis of whole-tumour heterogeneity were correlated with histopathologic tumour regression grade (TRG) and 5-year progression-free survival (PFS).

RESULTS

At baseline, high tumour F predicted good tumour response (TRG1-2) (AUC = 0.79, p = 0.01), with a sensitivity of 69% and a specificity of 100%. The combination of F and tumour volume (F_pre/V_pre) gave the highest prediction of poor tumour response (AUC = 0.93, p < 0.001) with a sensitivity of 88% and a specificity of 91%, and also predicted PFS (p < 0.01). Baseline tumour ADC was not significantly related to therapeutic outcome, whereas a positive change in ADC from baseline to after NACT, ΔADC, significantly predicted good tumour response (AUC = 0.83, p < 0.01, 83% sensitivity, 73% specificity), but not PFS.

CONCLUSIONS

The MRI parameter F/V at baseline was a remarkably strong predictor of both histopathologic tumour response and 5-year PFS in patients with LARC.

Value of intravoxel incoherent motion and dynamic contrast-enhanced MRI for predicting the early and short-term responses to chemoradiotherapy in nasopharyngeal carcinoma

The aim of the study was to investigate the value of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the early and short-term responses to chemoradiotherapy (CRT) in patients with nasopharyngeal carcinoma (NPC).Forty-three NPC patients underwent IVIM-DWI and DCE-MRI at baseline (pretreatment) and after the first cycle of induction chemotherapy (posttreatment). Based on whether locoregional lesions were identified, patients were divided into the residual and nonresidual groups at the end of CRT and into the good-responder and poor-responder groups 6 months after the end of CRT. The pretreatment and posttreatment IVIM-DWI parameters (ADC, D, D*, and f) and DCE-MRI parameters (K, Kep, and Ve) values and their percentage changes (Δ%) were compared between the residual and nonresidual groups and between the good-responder and poor-responder groups.None of perfusion-related parametric values derived from either DCE-MRI or IVIM-DWI showed significant differences either between the residual and nonresidual groups or between the good-responder and poor-responder groups. The nonresidual group exhibited lower pre-ADC, lower pre-D, and higher Δ%D values than did the residual group (all P <0.05). The good-responder group had lower pre-D and pre-ADC values than did the poor-responder group (both P <0.05). Based on receiver operating characteristic (ROC) curve analysis, pre-D had the highest area under the curve in predicting both the early and short-term responses to CRT for NPC patients (0.817 and 0.854, respectively).IVIM-DWI is more valuable than DCE-MRI in predicting the early and short-term response to CRT for NPC, and furthermore diffusion-related IVIM-DWI parameters (pre-ADC, pre-D, and Δ%D) are more powerful than perfusion-related parameters derived from both IVIM-DWI and DCE-MRI.

Emerging Role of MRI for Radiation Treatment Planning in Lung Cancer

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and allows for specific scanning sequences to optimize differentiation between various tissue types and properties. Moreover, it offers the potential for real-time motion imaging. This makes magnetic resonance imaging an ideal candidate imaging modality for radiation treatment planning in lung cancer. Although the number of clinical research protocols for the application of magnetic resonance imaging for lung cancer treatment is increasing (www.clinicaltrials.gov) and the magnetic resonance imaging sequences are becoming faster, there are still some technical challenges. This review describes the opportunities and challenges of magnetic resonance imaging for radiation treatment planning in lung cancer.

MITHRA - multiparametric MR/CT image adapted brachytherapy (MR/CT-IABT) in anal canal cancer: a feasibility study

PURPOSE

The aim of this study is to test a novel multiparametric imaging guided procedure for high-dose-rate brachytherapy in anal canal cancer, in order to evaluate the feasibility and safety.

MATERIAL AND METHODS

For this analysis, we considered all consecutive patients who underwent magnetic resonance/computed tomography image adapted brachytherapy (MR/CT-IABT) treated from February 2012 to July 2014. To conduct this project, we formed a working group that established the procedure and identified the indicators and benchmarks to evaluate the feasibility and safety. We considered the procedure acceptable if 90% of the indicators were consistent with the benchmarks. Magnetic resonance imaging with contrast and diffusion weighted imaging were performed with an MRI-compatible dummy applicator in the anus to define the position of the clinical target volume disease and biological information. A pre-implantation treatment planning was created in order to get information on the optimal position of the needles. Afterwards, the patient underwent a simulation CT and the definite post-implantation treatment planning was created.

RESULTS

We treated 11 patients (4 men and 7 women) with MR/CT-IABT and we performed a total of 13 procedures. The analysis of indicators for procedure evaluation showed that all indicators were in agreement with the benchmark. The dosimetric analysis resulted in a median of V200, V150, V100, V90, V85, respectively of 24.6%, 53.4%, 93.5%, 97.6%, and 98.7%. The median coverage index (CI) was 0.94, the median dose homogeneity index (DHI) was 0.43, the median dose non-uniformity ratio (DNR) resulted 0.56, the median overdose volume index (ODI) was 0.27. We observed no episodes of common severe acute toxicities.

CONCLUSIONS

Brachytherapy is a possible option in anal cancer radiotherapy to perform the boost to complete external beam radiotherapy (EBRT). Magnetic resonance can also have biological advantages compared to the US. Our results suggest that the multiparametric MR/CT-IABT for anal cancer is feasible and safe. This new approach paves the way to prospective comparison studies between MRI and ultrasound-guided brachytherapy (USBT) in anal canal cancer.