Pretreatment Apparent Diffusion Coefficient Research Articles

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

Apparent Diffusion Coefficient on Diffusion-Weighted Magnetic Resonance Imaging to Predict the Prognosis of Patients with Endometrial Cancer: A Meta-Analysis.

Apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (DWI) may help diagnose endometrial cancer (EC). However, the association between ADC and the recurrence and survival of EC remains unknown. We performed a systematic review and meta-analysis to investigate whether pretreatment ADC on DWI could predict the prognosis of women with EC. PubMed, Embase, and Cochrane's Library were searched for relevant cohort studies comparing the clinical outcomes between women with EC having low versus high ADC on pretreatment DWI. Two authors independently conducted data collection, literature searching, and statistical analysis. Using a heterogeneity-incorporating random-effects model, we analyzed the results. In the meta-analysis, 1358 women with EC were included from eight cohort studies and followed for a median duration of 40 months. Pooled results showed that a low pretreatment ADC on DWI was associated with poor disease-free survival (DFS, hazard ratio [HR]: 3.29, 95% CI: 2.04 to 5.31, p < 0.001; I2 = 41%). Subgroup analysis according to study design, tumor stage, MRI Tesla strength, ADC cutoff, follow-up duration, and study quality score showed consistent results (p for subgroup analysis all > 0.05). The predictive value of low ADC for poor DFS in women with EC decreased in multivariate studies compared to univariate studies (HR: 2.59 versus 32.57, p = 0.002). Further studies showed that a low ADC was also associated with poor overall survival (HR: 3.36, 95% CI: 1.33 to 8.50, p = 0.01, I2 = 0). In conclusion, a low ADC on pretreatment DWI examination may predict disease recurrence and survival in women with EC.

Factors associated with decreasing diffusion-weighted imaging-positive area volume after mechanical thrombectomy in patients with large early ischemic changes.

This study aimed to evaluate the factors associated with decreasing diffusion-weighted imaging (DWI) positive areas in patients with large early ischemic changes after mechanical thrombectomy (MT). This retrospective single-center clinical study was conducted between January 2013 and December 2022. We included consecutive patients who underwent MT for acute large-vessel occlusion of the anterior circulation with low pretreatment DWI-Alberta Stroke Program Early Computed Tomography Scores (ASPECTS) (0-5), effective recanalization [thrombolysis in cerebral infarction (TICI) 2b or TICI3], and magnetic resonance imaging (MRI) acquired before and after MT. We measured the DWI-positive area volume before and after MT. The primary endpoint was the after/before-MT DWI-positive area-volume ratio. In total, 28 patients were included in this study. Eight patients (29%) had an after/before-MT DWI-positive area-volume ratio of <1. The median mean apparent diffusion coefficient (ADC) levels of the DWI-positive areas in the groups with a ratio of <1 or >1 were 717 × 10⁶ mm2/s and 637 × 106 mm2/s, respectively (p = 0.011). Multivariate logistic regression analysis showed that ADC level (OR, 1.020 [95% confidence intervals (CIs), 1.001-1.040]; p = 0.040) was an independent predictor of a decreased DWI-positive area after MT. There was a negative correlation between the mean ADC level and the after/before-MT DWI-positive area-volume ratio (p < 0.001, |ρ| = 0.650), and the mean pretreatment ADC cutoff level was 649 × 106 mm2/s (area under the curve (AUC) = 0.806) for predicting a volume ratio of <1. The mean ADC level before-MT correlated with the after/before-MT DWI-positive area-volume ratio. A mean pretreatment ADC cutoff level of 649 × 106 mm2/s predicted a decreased DWI-positive area after MT.

Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma

Background and purposeDiffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response. Materials and methodsThirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease. ResultsADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed. ConclusionThe results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer.

To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps. Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models. The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference. The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation.

To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa). A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n=70) and test (n=30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision-recall (PR) curve analysis were used to analyze the models' performance. Sixty-five patients without BCR (BCR-) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR- and BCR+ groups or between the training and test datasets (all p>0.05). The AUCs were 0.637 (95% CI: 0.434-0.838), 0.841 (95% CI: 0.695-0.940), 0.840 (95% CI: 0.698-0.983), and 0.808 (95% CI: 0.627-0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different. The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purposeDiffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methodsAccuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. ResultsPhantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. ConclusionsThe acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer.

Dynamic biomarkers that permit the real-time monitoring of the tumor microenvironment response to therapy are an unmet need in breast cancer. Breast magnetic resonance imaging (MRI) has demonstrated value as a predictor of pathologic complete response and may reflect immune cell changes in the tumor microenvironment. The purpose of this pilot study was to investigate the value of breast MRI features as early markers of treatment-induced immune response. Fourteen patients with early HER2+ breast cancer were enrolled in a window-of-opportunity study where a single dose of trastuzumab was administered and both tissue and MRIs were obtained at the pre- and post-treatment stages. Functional diffusion-weighted and dynamic contrast-enhanced MRI tumor measures were compared with tumor-infiltrating lymphocytes (TILs) and RNA immune signature scores. Both the pre-treatment apparent diffusion coefficient (ADC) and the change in peak percent enhancement (DPE) were associated with increased tumor-infiltrating lymphocytes with trastuzumab therapy (r = -0.67 and -0.69, p < 0.01 and p < 0.01, respectively). Low pre-treatment ADC and a greater decrease in PE in response to treatment were also associated with immune-activated tumor microenvironments as defined by RNA immune signatures. Breast MRI features hold promise as biomarkers of early immune response to treatment in HER2+ breast cancer.

Pretreatment diffusion-weighted imaging for prediction of relapsed and refractory primary central nervous system lymphoma.

A subset of primary central nervous system lymphoma (PCNSL) has been shown to undergo an early relapsed/refractory (R/R) period after first-line chemotherapy. This study investigated the pretreatment clinical and MRI features to predict R/R in PCNSL, emphasizing the apparent diffusion coefficient (ADC) values in diffusion-weighted imaging (DWI). This retrospective study investigated the pretreatment MRI features for predicting R/R in PCNSL. Only patients who had undergone complete preoperative and postoperative MRI follow-up studies were included. From January 2006 to December 2021, 52 patients from two medical institutions with a diagnosis of PCNSL were included (median follow-up time, 26.3 months). Among these, 24 (46.2%) had developed R/R (median time to relapse, 13 months). Cox proportional hazard regression analyses were performed to determine hazard ratios for all parameters. Significant predictors of R/R in PCNSL were female sex, complete response (CR) to first-line chemotherapy, and ADC value/ratio (p < 0.05). Cut-off points of ADC values and ADC ratios for prediction of R/R were 0.68 × 10-3 mm2/s and 0.97, with AUCs of 0.78 and 0.77, respectively (p < 0.05). Multivariate Cox proportional hazards analysis showed that failure of CR to first-line chemotherapy and low ADC values (<0.68 × 10-3 mm2/s) were significant risk factors for R/R, with hazard ratios of 5.22 and 14.45, respectively (p < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted significantly shorter progression-free survival (p < 0.05). Pretreatment ADC values in DWI offer quantitative valuable information for the treatment planning in PCNSL.

Greater increases in intratumoral apparent diffusion coefficients after chemoradiotherapy predict better overall survival of patients with cervical cancer.

To evaluate whether 1) the intratumoral apparent diffusion coefficients (ADCs) change during cervical cancer treatment and 2) the pretreatment ADC values or their change after treatment predict the treatment outcome or overall survival of patients with cervical cancer. We retrospectively enrolled 52 patients with inoperable cervical cancer treated with chemoradiotherapy, who had undergone diffusion weighted MRI before treatment and post external beam radiotherapy (EBRT) and concurrent chemotherapy. A subgroup of patients (n = 28) underwent altogether six consecutive diffusion weighted MRIs; 1) pretreatment, 2) post-EBRT and concurrent chemotherapy; 3-5) during image-guided brachytherapy (IGBT) and 6) after completing the whole treatment course. To assess interobserver and intertechnique reproducibility two observers independently measured the ADCs by drawing freehand a large region of interest (L-ROI) covering the whole tumor and three small ROIs (S-ROIs) in areas with most restricted diffusion. Reproducibility was equally good for L-ROIs and S-ROIs. The pretreatment ADCs were higher in L-ROIs (883 mm2/s) than in S-ROIs (687 mm2/s, P < 0.001). The ADCs increased significantly between the pretreatment and post-EBRT scans (L-ROI: P < 0.001; S-ROI: P = 0.001). The ADCs remained significantly higher than pretreatment values during the whole IGBT. Using S-ROIs, greater increases in ADCs between pretreatment and post-EBRT MRI predicted better overall survival (P = 0.018). ADC values significantly increase during cervical cancer treatment. Greater increases in ADC values between pretreatment and post-EBRT predicted better overall survival using S-ROIs. Standardized methods for timing and delineation of ADC measurements are advocated in future studies.

Feasibility of high-resolution readout-segmented echo-planar imaging with simultaneous multislice imaging in assessing rectal cancer.

To investigate the feasibility of high-resolution readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) imaging to predict well-differentiated rectal cancer.Kindly check and confirm whether the Author Name 'Hongyun Huang ' is correctly identified.confirm METHODS: A total of eighty-three patients with nonmucinous rectal adenocarcinoma received both prototype SMS high-spatial-resolution and conventional rs-EPI sequences. Image quality was subjectively assessed by two experienced radiologists using a 4-point Likert scale (1 = poor, 4 = excellent). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of the lesion were measured by two experienced radiologists in the objective assessment. Paired t tests or Mann‒Whitney U tests were used to compare the two groups. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to determine the predictive value of the ADCs in discriminating well-differentiated rectal cancer in the two groups. A two-sided p value < 0.05 represented statistical significance.Please check and confirm if the authors and affiliation details have been correctly identified. Amend if necessary.confirm RESULTS: In the subjective assessment, high-resolution rs-EPI had better image quality than conventional rs-EPI (p < 0.001). High-resolution rs-EPI also had a significantly higher SNR and CNR (p < 0.001). The T stage of rectal cancer was inversely correlated with the ADCs measured on high-resolution rs-EPI (r = -0.622, p < 0.001) and rs-EPI (r = -0.567, p < 0.001). The AUC of high-resolution rs-EPI in predicting well-differentiated rectal cancer was 0.768. High-resolution rs-EPI with SMS imaging provided significantly higher image quality, SNRs, and CNRs and more stable ADC measurements than conventional rs-EPI. Additionally, the pretreatment ADC on high-resolution rs-EPI could discriminate well-differentiated rectal cancer.

Isotoxic dose escalated radiotherapy for glioblastoma based on diffusion-weighted MRI and tumor control probability-an in-silico study.

Glioblastoma (GBM) is the most common malignant primary brain tumor with local recurrence after radiotherapy (RT), the most common mode of failure. Standard RT practice applies the prescription dose uniformly across tumor volume disregarding radiological tumor heterogeneity. We present a novel strategy using diffusion-weighted (DW-) MRI to calculate the cellular density within the gross tumor volume (GTV) in order to facilitate dose escalation to a biological target volume (BTV) to improve tumor control probability (TCP). The pre-treatment apparent diffusion coefficient (ADC) maps derived from DW-MRI of ten GBM patients treated with radical chemoradiotherapy were used to calculate the local cellular density based on published data. Then, a TCP model was used to calculate TCP maps from the derived cell density values. The dose was escalated using a simultaneous integrated boost (SIB) to the BTV, defined as the voxels for which the expected pre-boost TCP was in the lowest quartile of the TCP range for each patient. The SIB dose was chosen so that the TCP in the BTV increased to match the average TCP of the whole tumor. By applying a SIB of between 3.60 Gy and 16.80 Gy isotoxically to the BTV, the cohort's calculated TCP increased by a mean of 8.44% (ranging from 7.19 to 16.84%). The radiation dose to organ at risk is still under their tolerance. Our findings indicate that TCPs of GBM patients could be increased by escalating radiation doses to intratumoral locations guided by the patient's biology (i.e., cellularity), moreover offering the possibility for personalized RT GBM treatments. A personalized and voxel level SIB radiotherapy method for GBM is proposed using DW-MRI, which can increase the tumor control probability and maintain organ at risk dose constraints.

Response assessment of locally advanced rectal cancer after neoadjuvant chemoradiotherapy: Is apparent diffusion coefficient useful on 3T magnetic resonance imaging?

To assess the diagnostic value of apparent diffusion coefficient (ADC) on 3T device for the prediction of tumoral response to neoadjuvant chemoradiotherapy (nCRT) and for the response assessment after nCRT in patients with locally advanced rectal cancer (LARC), using pathology as a reference. Forty-one patients affected by LARC undergoing 3.0T MRI before and after nCRT were retrospectively selected. After the conventional acquisition of high resolution T2-weighted sequences, diffusion-weighted MRI (DW-MRI) was performed using a spin-echo echo-planar sequence with multiple b values (150, 500, 1000, 1500 s/mm2 ). Fitted ADC values were calculated for each rectal lesion before and after nCRT by drawing a hand-made region of interest (ROI) around the tumour outline. All patients underwent surgery and pathological staging (classified according to tumour regression grading [TRG] and to tumour and node [TN]) represented the reference standard. Pretreatment ADC value (pre-ADC), ADC value obtained after nCRT (post-ADC) and the difference between post-ADC and pre-ADC (ΔADC) were correlated with both the TRG classes and the TN staging system in each patient. The ADC values obtained in the post nCRT examination and the ΔADC were statistically related both to TRG (p=0.0004; p=0.0126, respectively) and TN (p=0.0484; p=0.0673, respectively) stages at histopathology. On the contrary, the pre-ADC was not related either to the TRG classes or to the lesion TN staging system (p > 0.05). 3 T DW-MRI using ADC value can be useful to assess the efficacy of nCRT in LARC; in fact, post-ADC and ΔADC values improve MR capability to evaluate tumour response.

Prospective Assessment of Diffusion-Weighted-MRI as a Biomarker of Treatment Response and Disease Control in Head and Neck Cancer

<h3>Purpose/Objective(s)</h3> To determine diffusion-weighted MRI (DWI) parameters associated with tumor response and local control (LC) in head and neck (HNC) patients treated with definitive radiation therapy (RT). <h3>Materials/Methods</h3> Eighty-seven HNC patients enrolled in an active prospective imaging study at MD Anderson Cancer Center were included in the analysis. Patients had MRIs prior to, at mid-RT, and post-treatment completion. Inclusion criteria include adult with histologic evidence of malignant head and neck neoplasm indicated for curative-intent treatment with RT with/without chemotherapy; good performance status (ECOG score 0-2); and with no contraindications to MRI. Patients were scanned using a full-body MRI scanner with two large four-channel flex phased-array coils. We used fat-suppressed T2-weighted turbo spin echo for tumor segmentation which was co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Sequence parameters were (TR/TE (ms); pixel size/slice thickness (mm); b-values (s/mm2)): T2-weighted (5280/80; 0.5/2; N/A), BLADE (5900/50; 2/4; 0 & 800), RESOLVE (9380/63; 2/4; 0 & 800). Treatment response was assessed at mid-RT and at 8-12 weeks post-RT using RECIST 1.1 criteria and was defined as: complete response (CR) vs non-complete response (non-CR). Pre-RT ADC was correlated with RT response (CR vs non-CR) at mid- and post-RT time points. Mann-Whitney test was used to compare ADC values between the mid-treatment CR group and the non-CR group. Recursive partitioning analysis (RPA) was done to identify ADC threshold associated with relapse. <h3>Results</h3> Fifty-nine patients with grossly visible primary tumor volume prior to RT start were included in this analysis. Patients' disease and treatment criteria are shown in Table 1. Pre-treatment ADC was not correlated with tumor response or oncologic outcomes (P>0.05). For patients with CR at the end of RT, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10^–3 mm²/s <i>vs</i> (1.37 ± 0.22) × 10^–3 mm²/s, <i>p</i> <0.0001). However, patients with no-CR had no statistically significant increase (p>0.05). Median follow-up was 32 months. Patients with ≤106% delta ADC at mid-RT had significantly worse local control at 2-year compared to patients with higher delta ADC (48% vs 96%, <i>p</i> <0.0001). <h3>Conclusion</h3> Delta ADC changes is a strong predictor of local recurrence in head and neck cancer patients. Multi-institutional data is needed for validation of our results.

Intra-Treatment Tumor Apparent Diffusion Coefficient, a Quantitative Imaging Metric, is Associated with Neck Nodal Recurrence in De-Escalated Treatment of HPV-Positive Oropharyngeal Cancer (OPC)

<h3>Purpose/Objective(s)</h3> De-escalation strategies for the treatment of HPV-associated OPC show promise for effective, less toxic treatment. However, identifying accurate markers of more aggressive subsets of HPV OPC remains challenging. The 30 Reduction in Radiation for OPC (ROC) trial prospectively used intra-treatment ¹⁸F-FMISO imaging to selectively de-escalate patients to 30Gy chemoradiotherapy, however, a subset of patients still developed nodal recurrence. We hypothesized that diffusion-weighted (DW) MRI-derived quantitative imaging metrics, such as apparent diffusion coefficient (ADC), a surrogate marker of tumor cellularity based on water diffusion patterns, might help identify patients at high risk of recurrence and enable broader use of de-escalation strategies<b>.</b> <h3>Materials/Methods</h3> The 30 ROC trial enrolled patients with p16-positive OPC, cT0-2, N1-2c (AJCC 7^th ed). All patients had pre-treatment ¹⁸F-FMISO (fluoromisonidazole) PET scan and those with baseline nodal hypoxia underwent a 2^nd intra-treatment scan. All patients underwent primary site surgery where negative margin was not required. All patients had gross neck nodal disease. Those with intra-treatment resolution of nodal hypoxia or without initial hypoxia were de-escalated to 30Gy (non-hypoxic group) with 2 cycles of concurrent chemotherapy, while those with persistent radiographic hypoxia (hypoxic group) were treated to 70Gy. Standard T1, T2 weighted, and multiple b-value DW-MRI data were acquired pre-treatment and weekly during the first 4 weeks of treatment. Two radiation oncologists delineated nodal tumor regions of interest (ROIs) in a blinded fashion with ITK-SNAP on DW images (b= 0 s/mm²). Multiple b-value DW data were fitted to a monoexponential model to calculate ADC (mm²/s). The data analysis was performed on ROIs and voxel-wise using the in-house developed tool MRI-QAMPER. Standard statistical analysis was performed using MRI and clinical response data based on RECISTv1.1 provided by a radiologist. <h3>Results</h3> 158 patients were enrolled, of whom 95 had pre-treatment DW-MRI data available with tumor ROI with ADC values. Based on overall ¹⁸F-FMISO imaging, 12.6% (12/95) exhibited persistent hypoxia and 87.4% (83/95) had resolved/no hypoxia. All patients with nodal recurrences were in the de-escalated non-hypoxic group (8/83). Pre-treatment ADC was not different between overall hypoxic and non-hypoxic groups. Among de-escalated patients, those who developed nodal recurrence exhibited significantly different intra-treatment ADC at week 3 (before completing 30Gy) relative to those who did not recur (P<0.05, Wilcoxon rank-sum test). <h3>Conclusion</h3> The quantitative imaging metric, ADC, reflects both the restricted and hindered Brownian motion of water molecules in tumor tissue. Thus, ADC may be a useful intra-treatment marker to identify patients at risk for nodal recurrence to benefit from the incorporation of an upfront planned neck dissection after 30Gy as part of the de-escalation strategy.

Development of a Radiomic Signature Derived from Apparent Diffusion Coefficient Maps to Predict Outcomes in Cervical Cancer

<h3>Purpose/Objective(s)</h3> Previous studies have suggested that apparent diffusion coefficient (ADC) values from pretreatment MR are associated with outcomes after definitive chemoradiation in patients with cervical cancer. The purpose of this study was to determine whether radiomic features derived from ADC maps on pretreatment diffusion-weighted MRI are predictive of patient outcomes, and prioritize those individual features into a predictive signature. <h3>Materials/Methods</h3> ADC maps were generated from pretreatment pelvic diffusion-weighted MRIs acquired on a 1.5T MRI simulator for twenty-six patients prior to definitive radiotherapy (RT). All patients were treated with curative intent chemoradiation, including external beam RT and image guided brachytherapy as per the standard of care. Median post therapy clinical follow up time was 67 months, and treatment outcomes were approximated by the results of 3-month post therapy FDG-PET. The clinical endpoint was recurrence-free survival (RFS), defined as death or recurrence of cancer. High risk regions, visually identified as areas of ADC hypointensity, were manually contoured. Quantitative radiomic features characterizing intensity, shape, texture and multiscale wavelet transformations of contoured regions of interest were identified using an unsupervised learning approach. A radiomic signature for RFS was generated using a multivariate Cox model that included the best performing feature from each of the four categories. <h3>Results</h3> Our model identified high and low risk groups based on a radiomic signature that stratified patients by RFS (hazard ratio 4.5, p=0.0016). The features chosen for the signature included the median of voxel intensity, joint entropy of the gray level co-occurrence matrix (GLCM), elongation of the region of interest, and LHH wavelet transform of intensity skewness. The mean receiver-operator curve on 5-fold cross-validation had an area under the curve of 0.83 +/- 0.16. <h3>Conclusion</h3> In this pilot study, we have developed a radiomic signature derived from pretreatment ADC maps of cervix tumors that successfully stratifies this group of uniformly treated patients by RFS. Future work will seek to validate our model on a larger independent data set to determine its predictive value as well as correlate these data with radiomic features from FDG-PET/CT images and RNA-seq data from patient tumor samples.

THE ROLE OF DIFFUSION-WEIGHTED MR IMAGING IN EVALUATING THE RESPONSE TO STEROID THERAPY IN IDIOPATHIC GRANULOMATOUS MASTITIS LESIONS

Objective: The aim of this study was to investigate the role of diffusion-weighted magnetic resonance imaging (DW MRI) with an apparent diffusion coefficient (ADC) map in evaluating the response to treatment of steroid-treated idiopathic granulomatous mastitis (IGM) lesions. Materials and Methods: This retrospective study included 99 lesions of 58 female patients (average age: 32.91 years; range: 22–55 years) with biopsy-proven IGM. Patients were treated with oral and topical steroids. All pre-treatment and post-treatment MR examinations were evaluated. The maximal size of the masses and nonmass enhancement (NME) lesions were measured. Patients were classified as complete response (CR), partial response (PR), and non-response (NR) according to the dynamic contrast-enhanced (DCE) MR findings after treatment. Results: ADC values of areas occupied by IGM (0.933±0.317x10-3 mm2 /sec) were lower than contralateral normal parenchyma (1.259±0.423x10-3 mm2 /sec). Twenty-two (22.22%) of the lesions were in the NR group, 30 (30.30%) in the PR group, and 47 (47.47%) in the CR group. There was no significant difference between the pre-treatment ADC values in NR, PR, and CR groups (p=0.228). There was a significant difference between the pre-treatment and post-treatment ADC values in the PR groups (p=0.001). Conclusion: DW MR imaging in IGM is a useful method to monitor the response to treatment. However, it is not successful in predicting response to treatment.

Prediction of response to radiotherapy in locally advanced carcinoma cervix using multiparametric MRI: A prospective, single-center, longitudinal study.

To evaluate the possible role of a multiparametric magnetic resonance imaging (MRI) and semiquantitative fusion map for the prediction of response to radiotherapy in carcinoma cervix. This was a prospective, single-center, longitudinal observational study performed on patients with locally advanced carcinoma cervix. Relative blood flow (rBF), relative blood volume (rBV), and apparent diffusion coefficient (ADC) values were obtained before and after the onset of radiotherapy. rBV, rBF, and ADC values were used to generate a semiquantitative pharmacokinetic model map to identify any hypoxic component of the tumor. The subjects were retrospectively classified as responders and nonresponders based on response to treatment. Prospective prediction of response status was done using pretreatment multiparametric MRI parameters (rBF, rBV, and ADC) and semiquantitative parametric map. In 32 patients (29 with parametrial involvement and 15 with lymphadenopathy), pretreatment ADC of the primary tumor was the most accurate measure for predicting response to treatment as well as for treatment-induced fractional volume reduction. Although rBV and rBF were insignificant metrics in isolation for predicting response status, a combination with ADC in the form of parametric map had a sensitivity of 86.4% and 77.2%, specificity of 70% and 70%, positive predictive value of 86.4% and 85%, and negative predictive value 70% and 59% respectively by two independent observers. ADC is the most accurate measure for predicting the response to treatment. A manual parametric map generated by an off-line fusion of the above map with those generated by pharmacokinetic modeling of perfusion-weighted MRI may be a useful tool for the prediction of response to radiotherapy.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.

Simple SummaryMetachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Interstitial Brachytherapy of the Liver for Renal Cell Carcinoma: ADC Measurements Do Not Predict Overall Survival.

To assess the influence of pre-treatment apparent diffusion coefficient (ADC) measurements on outcomes in patients undergoing interstitial brachytherapy (iBT) for liver metastases from renal cell carcinoma. Patients undergoing iBT for renal cell carcinoma (RCC) liver metastases were retrospectively identified. Patients were eligible for inclusion if they had a pre-treatment magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI) sequences. For each lesion, a region of interest (ROI) was placed along the contours of the entire lesion across all slices. For each ROI, ADC minimum, mean and maximum as well as the lesion area were noted, and the average was calculated for each lesion. ADC measurements were correlated with overall survival. The analysis included 17 patients. Median overall survival was 36 months. Neither ADC measurement was significantly associated with overall survival. ADC min (HR=1.00, 95%CI=1.00-1.00, p=0.600), ADC max (HR=1.001, 95%CI=0.998-1.003, p=0.490), ADC mean (HR=0.999, 95%CI=0.996-1.003, p=0.638). ADC is not able to differentiate between groups with good and bad overall survival in patients undergoing iBT for RCC liver metastases.