Tumor volume reduction rate is superior to RECIST for predicting the pathological response of rectal cancer treated with neoadjuvant chemoradiation: Results from a prospective study

Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

In our recent study, we explored the efficacy of three-dimensional (3D) measurement of tumor volume in predicting the improvement of quality of life (QoL) in patients suffering from renal cell cancer (RCC), who were treated with axitinib and anti-PD-L1 antibodies. This study encompassed 18 RCC patients, including 10 men and 8 women, with an average age of 56.83 ± 9.94 years. By utilizing 3D Slicer software, we analyzed pre- and post-treatment CT scans to assess changes in tumor volume. Patients' QoL was evaluated through the FKSI-DRS questionnaire. Our findings revealed that 3D models for all patients were successfully created, and there was a moderate agreement between treatment response classifications based on RECIST 1.1 criteria and volumetric analysis (kappa = 0.556, p = 0.001). Notably, nine patients reported a clinically meaningful improvement in QoL following the treatment. Interestingly, the change in tumor volume as indicated by the 3D model showed a higher area under the curve in predicting QoL improvement compared to the change in diameter measured by CT, although this difference was not statistically significant (z = 0.593, p = 0.553). Furthermore, a multivariable analysis identified the change in tumor volume based on the 3D model as an independent predictor of QoL improvement (odds ratio = 1.073, 95% CI 1.002-1.149, p = 0.045).In conclusion, our study suggests that the change in tumor volume measured by a 3D model may be a more effective predictor of symptom improvement in RCC patients than traditional CT-based diameter measurements. This offers a novel approach for assessing treatment response and patient well-being, presenting a significant advancement in the field of RCC treatment.

Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow

Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data

PURPOSE

To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods.

METHODS

Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models.

RESULTS

Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2.

CONCLUSION

There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.

Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review

Simple Summary

The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers.

Abstract

Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.

Comparison of response evaluation criteria in solid tumors and tumor regression grade in evaluating the effect of preoperative systemic therapy of gastric cancer

BACKGROUND

Both Response Evaluation Criteria in Solid Tumors (RECIST) and tumor regression grade (TRG) play key roles in evaluating tumor response. We analyzed the consistency of TRG and RECIST 1.1 for gastric cancer (GC) patients and compared their prognostic values.

METHODS

Patients with GC who received preoperative chemotherapy or chemoimmunotherapy and had records of TRG from December 2013 to October 2021 were enrolled retrospectively. TRG 0-1 and 2-3 are considered as corresponding to complete response (CR)/partial response (PR) and stable disease (SD)/progress disease (PD) in RECIST 1.1, respectively. The primary endpoints were disease-free survival (DFS) and overall survival (OS). The consistency of RECIST and TRG was examined by kappa statistics. Survival analysis was performed using the Kaplan Meier method.

RESULT

One hundred fifty seven GC patients were enrolled, including 125 with preoperative chemotherapy and 32 with chemoimmunotherapy. Among them, 56 patients had measurable lesions. Only 19.6% (11/56) of the patients had consistent results between RECIST 1.1 and TRG. TRG was correlated with both OS and DFS (P = 0.02 and 0.03, respectively) while response according to RECIST1.1 was not (P = 0.86 and 0.23, respectively). The median DFS had not reached in the TRG 0-1 group and was 16.13 months in TRG 2-3 group. TRG 2-3 was associated with young age and peritoneal or liver metastasis. Besides, preoperative chemoimmunotherapy had a significantly higher pCR rate than chemotherapy alone (34.4% vs 8.0%, P < 0.001).

CONCLUSION

TRG was in poor agreement with RECIST 1.1. TRG was better than RECIST 1.1 in predicting DFS and OS for GC patients who received preoperative therapy.

The predictive value of tumor volume reduction ratio on three-dimensional endorectal ultrasound for tumor response to chemoradiotherapy for locally advanced rectal cancer

Background

Preoperative chemoradiotherapy remains part of the standard treatment for patients with locally advanced rectal cancer. Subsequent treatment individualization requires accurate prediction of tumor response to chemoradiotherapy. Three-dimensional endorectal ultrasound (3D-ERUS) can automatically capture and store the images of the rectal wall and rectal cancer with high resolution. In this study, we aimed to assess the correlation and predictive value between tumor volume changes measured on 3D-ERUS and the histopathological tumor response after chemoradiotherapy for patients with locally advanced rectal cancer.

Methods

A total of 54 patients with locally advanced rectal cancer who underwent chemoradiotherapy and had complete 3D-ERUS data pre-and post-chemoradiotherapy were enrolled in the study. The tumor volume pre-and post-chemoradiotherapy was measured manually on 3D-ERUS, and the tumor volume reduction ratio was calculated. The histopathological tumor regression grade (TRG) was used to assess tumor response. The differences in volumetry parameters were compared between groups with varying tumor response. The diagnostic efficacy of the tumor volume reduction ratio was evaluated by the receiver operating characteristic (ROC) curve.

Results

The mean age of all patients was 55.19±12.46 years. The relative proportions of TRG 0–3 were 29.6% (16/54), 16.6% (9/54), 50% (27/54), and 3.8% (2/54), respectively. The median tumor volumes post-chemoradiotherapy in good responders (TRG 0–1, median tumor volume =3.26 cm³) and the complete response group (TRG 0, median tumor volume =2.61 cm³) were smaller than those in poor responders (TRG 2–3, median tumor volume =5.43 cm³) and the partial response group (TRG 1–3, median tumor volume =4.00 cm³), while tumor volume reduction ratios were higher than those of poor responders (79.32% vs. 59.67%) and the partial response group (82.22% vs. 61.64%), with significant differences (all P values <0.05). The ROC curves showed that the cut-off values of the tumor volume reduction ratio to predict good responders and complete response were 67.77% and 72.02%, respectively. The corresponding areas under the curve in the prediction of good responders and complete response were 0.830 and 0.829, respectively.

Conclusions

The tumor volume reduction ratio measured on 3D-ERUS might be a helpful indicator for tumor response in patients with locally advanced rectal cancer.

CSF1 receptor inhibition of tenosynovial giant cell tumor using novel disease-specific MRI measures of tumor burden

Aim: Monitoring treatment of tenosynovial giant cell tumor (TGCT) is complicated by the irregular shape and asymmetrical growth of the tumor. We compared responses to pexidartinib by Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 with those by tumor volume score (TVS) and modified RECIST (m-RECIST). Materials & methods: MRIs acquired every two cycles were assessed centrally using RECIST 1.1, m-RECIST and TVS and tissue damage score (TDS). Results: Thirty-one evaluable TGCT patients were treated with pexidartinib. From baseline to last visit, 94% of patients (29/31) showed a decrease in tumor size (median change: -60% [RECIST], -66% [m-RECIST], -79% [TVS]). All methods showed 100% disease control rate. For TDS, improvements were seen in bone erosion (32%), bone marrow edema (58%) and knee effusion (46%). Conclusion: TVS and m-RECIST offer potentially superior alternatives to conventional RECIST for monitoring disease progression and treatment response in TGCT. TDS adds important information about joint damage associated with TGCT.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm

OBJECTIVE

The aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC).

METHODS

Two populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification. Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance. The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated.

RESULTS

Four features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833). Both inter- and intra-reader evaluation showed good agreement (κ > 0.6).

CONCLUSIONS

The proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.

Efficacy of Endoscopic Ultrasound-Guided Ablation with the HybridTherm Probe in Locally Advanced or Borderline Resectable Pancreatic Cancer: A Phase II Randomized Controlled Trial

Endoscopic ultrasound-ablation with HybridTherm-Probe (EUS-HTP) significantly reduces tumour volume (TV) in locally-advanced pancreatic ductal adenocarcinoma (LA-PDAC). We aimed at investigating the clinical efficacy of EUS-HTP plus chemotherapy versus chemotherapy (HTP-CT and CT arms) in LA- and borderline-resectable (BR) PDAC, with 6-months progression-free survival (6-PFS) rate as primary endpoint. In a phase-II randomized-controlled-trial, 33 LA/BR-PDAC patients per-arm were planned to verify 20% improved 6-PFS rate. Radiological response (Choi criteria), TV and serum CA19.9 were assessed up to 6-months. Seventeen and 20 LA/BR-PDAC patients were randomized to HTP-CT or CT. Baseline and CT-related features were balanced. At 6-months, 6-PFS rate was 41.2% and 30% in HTP-CT and CT arms (p = 0.48), respectively. A decrease ≥50% of serum CA19.9 was achieved in 75% and 64.3% of HTP-CT and CT patients (p = 0.53), respectively. TV reduced up to 6-months in 64.3% and 47.1% of HTP-CT and CT patients (p = 0.35), respectively. Resection rate, PFS-time and overall survival (OS-time) were similar. HTP-CT achieves a non-significant 11.2%, 10.7% and 17.2% improved 6-PFS, CA19.9 decrease ≥50% and TV reduction rates over CT, without any impact on resection rate, PFS-time and OS-time. As the study was underpowered, these results suggest further investigation of EUS-local ablation in selected patients with localized disease after induction CT.

Semi-Automatic Volumetric and Standard Three-Dimensional Measurements for Primary Tumor Evaluation and Response to Treatment Assessment in Pediatric Rhabdomyosarcoma

Current prognostic classification of rhabdomyosarcoma in children requires precise measurements of the tumor. The purpose of the study was to compare the standard three-dimensional (3D) measurements with semi-automatic tumor volume measurement method concerning assessment of the primary tumor size and the degree of response to treatment for rhabdomyosarcoma in children. Magnetic Resonance Imaging data on 31 children with treated rhabdomyosarcoma based on the Cooperative Weichteilsarkom Studiengruppe (CWS) guidance was evaluated. Tumor sizes were measured by two methods: 3D standard measurements and semi-automatic tumor volume measurement (VOI) at diagnosis, and after 9 and 17/18 weeks of the induction chemotherapy. Response to treatment and prediction values were assessed. The tumor volume medians calculated using VOI were significantly higher in comparison with those calculated using the 3D method both during the diagnosis as well as after 9 weeks of the chemotherapy and during the 17-18th week of the treatment. The volume measurements based on the generalized estimating equations on the VOI method were significantly better than the 3D method (p = 0.037). The volumetric measurements alone can hardly be considered an unequivocal marker used to make decisions on modification of the therapy in patients with rhabdomyosarcoma.

Predicting Response to Total Neoadjuvant Treatment (TNT) in Locally Advanced Rectal Cancer Based on Multiparametric Magnetic Resonance Imaging: A Retrospective Study

Purpose

To investigate the potential value of magnetic resonance imaging (MRI) in predicting response relevance to total neoadjuvant treatment (TNT) in locally advanced rectal cancer.

Methods

We analyzed MRI of 71 patients underwent TNT from 2015 to 2017 retrospectively. We categorized the response of TNT as CR (complete response) vs non-CR, and high vs moderate vs low sensitivity. Logistic regression analysis was used to identify the best predictors of response. Diagnostic performance was assessed using receiver operating characteristic curve analysis.

Results

Post-ICT (induction chemotherapy) ∆TL (tumor length), post-CRT (concurrent chemoradiotherapy) ∆LNN (the numbers of lymph node metastases), post-CCT (consolidation chemotherapy) ∆S_DWI (maximum cross-sectional area of tumor on diffusion-weighted imaging), post-CCT ADC_T (the mean apparent diffusion coefficient values of tumor) and post-CCT ∆LNV (volume of lymph node) were the best CR predictors. Post-ICT ∆TL, post-CRT EMVI (extramural vascular invasion) and post-CCT ∆S_T2 (S on T2-weight) were the best significant factors for high sensitivity.

Conclusion

Post-ICT ∆TL may be an early predictor of CR and high sensitivity to TNT. Dynamic analysis based on MRI between baseline and post-CCT could provide the most valuable prediction of CR. The grouping modality of CR vs non-CR may be more suitable for treatment response prediction than high vs moderate vs low sensitivity.

Clinical 3D Imaging of the Anterior Segment With Ultrasound Biomicroscopy

Purpose

Ultrasound biomicroscopy (UBM) is an important ophthalmic imaging modality due to its ability to see behind pigmented iris and to visualize anterior chamber when the eye's transparency is compromised. We created a three-dimensional UBM (3D-UBM) system and acquired example images to illustrate its potential.

Methods

A commercial 50-MHz two-dimensional UBM (2D-UBM) system was attached to a precision translation stage and translated across the eye to acquire an image volume. The stage was mounted on a surgical microscope, which enabled safe, stable positioning. Image processing steps included image alignment, noise reduction, and calibration. 3D visualization included alignment of the optic axis, multiplanar reformatting at arbitrary orientations, and volume rendering with optimized transfer functions. Scans were performed on cadaver and rabbit eyes.

Results

3D-UBM allowed visualization of the anterior segment tissues within a 3D anatomical context, unlike 2D-UBM. En face views and interactive slicer operations suggested an ability to plan and assess treatments, including lens placement and microcatheter cannulation of Schlemm's canal. Interactive software allowed us to make accurate measurements of tissue structures (e.g., iridocorneal angles, cyst volumes). In addition, unique measurements of ciliary tissues included single ciliary process volumes of 0.234 ± 0.093 mm³ with surface areas of 3.02 ± 1.07 mm² and ciliary muscle volume of 67.87 mm³.

Conclusions

3D-UBM imaging of the anterior segment can be used to enable unique visualization and quantification of anterior segment structures.

Translational Relevance

3D-UBM provides informative 3D imaging of tissues in the eye that are invisible to light to potentially provide physicians with improved diagnosis, treatment planning, and treatment assessment as compared to conventional 2D-UBM.

Prognostic Value of Tumor Regression Grade on MR in Rectal Cancer: A Large-Scale, Single-Center Experience

Objective

To determine the prognostic value of MRI-based tumor regression grading (mrTRG) in rectal cancer compared with pathological tumor regression grading (pTRG), and to assess the effect of diffusion-weighted imaging (DWI) on interobserver agreement for evaluating mrTRG.

Materials and Methods

Between 2007 and 2016, we retrospectively enrolled 321 patients (male:female = 208:113; mean age, 60.2 years) with rectal cancer who underwent both pre-chemoradiotherapy (CRT) and post-CRT MRI. Two radiologists independently determined mrTRG using a 5-point grading system with and without DWI in a one-month interval. Two pathologists graded pTRG using a 5-point grading system in consensus. Kaplan-Meier estimation and Cox-proportional hazard models were used for survival analysis. Cohen's kappa analysis was used to determine interobserver agreement.

Results

According to mrTRG on MRI with DWI, there were 6 mrTRG 1, 48 mrTRG 2, 109 mrTRG 3, 152 mrTRG 4, and 6 mrTRG 5. By pTRG, there were 7 pTRG 1, 59 pTRG 2, 180 pTRG 3, 73 pTRG 4, and 2 pTRG 5. A 5-year overall survival (OS) was significantly different according to the 5-point grading mrTRG (p = 0.024) and pTRG (p = 0.038). The 5-year disease-free survival (DFS) was significantly different among the five mrTRG groups (p = 0.039), but not among the five pTRG groups (p = 0.072). OS and DFS were significantly different according to post-CRT MR variables: extramural venous invasion after CRT (hazard ratio = 2.259 for OS, hazard ratio = 5.011 for DFS) and extramesorectal lymph node (hazard ratio = 2.610 for DFS). For mrTRG, k value between the two radiologists was 0.309 (fair agreement) without DWI and slightly improved to 0.376 with DWI.

Conclusion

mrTRG may predict OS and DFS comparably or even better compared to pTRG. The addition of DWI on T2-weighted MRI may improve interobserver agreement on mrTRG.

Necrosis volume and Choi criteria predict the response to endoscopic ultrasonography-guided HybridTherm ablation of locally advanced pancreatic cancer

Background and study aims  Endoscopic ultrasound (EUS)-guided ablation of pancreatic ductal adenocarcinoma (PDAC) with HybridTherm-Probe (EUS-HTP) is feasible and safe, but the radiological response and ideal tool to measure it have not been investigated yet. The aims of this study were to: 1) assess the radiological response to EUS-HTP evaluating the vital tumor volume reduction rate, Response Evaluation Criteria in Solid Tumors (RECIST1.1) and Choi criteria; 2) determine the prognostic predictive yield of these criteria. Patients and methods  A retrospective analysis was performed of patients with locally advanced PDAC after primary treatment or unfit for chemotherapy prospectively treated by EUS-HTP. Computed tomography scan was performed 1 month after EUS-HTP to evaluate: 1) vital tumor volume reduction rate (VTVRR) by measuring necrosis and tumor volumes through a computer-aided detection system; and 2) RECIST1.1 and Choi criteria. Results  EUS-HTP was feasible in 22 of 31 patients (71 %), with no severe adverse events. Median post-HTP survival was 7 months (1 - 35). Compared to pre-HTP tumor volume, a significant 1-month VTVRR (mean 21.4 %) was observed after EUS-HTP ( P  = 0.005). We identified through ROC analysis a VTVRR > 11.46 % as the best cut-off to determine post-HTP 6-month survival outcome (AUC = 0.733; sensitivity = 70.0 %, specificity = 83.3 %). This cut-off was significantly associated with longer overall survival (HR = 0.372; P  = 0.039). According to RECIST1.1 and Choi criteria, good responders to EUS-HTP were 60 % and 46.7 %, respectively. Good responders according to Choi, but not to RECIST1.1, had longer survival (HR = 0.407; P  = 0.04). Conclusions  EUS-HTP induces a significant 1-month VTVRR. This effect is assessed accurately by evaluation of necrosis and tumor volumes. Use of VTVRR and Choi criteria, but not RECIST 1.1 criteria, might identify patients who could benefit clinically from EUS-HTP.

MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry

AIM

To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (V_T2W) and on DWI at b=1,000 images (V_b,1,000) at each time point, hence the tumour volume reduction rate (ΔV_T2W and ΔV_b,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves.

RESULTS

After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔV_T2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔV_T2W (-82.8%) and ΔV_{b, 1,000} (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively.

CONCLUSIONS

The median ADC value at pre-treatment MRI and ΔV_T2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔV_T2W and ΔV_b,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.

Risk factor analysis for inaccurate pre-operative MRI staging in rectal cancer

BACKGROUND

Various tumor characteristics might lead to inaccurate local MRI-defined stage of rectal cancer and the purpose of this study was to explore the clinicopathological factors that impact on the precision pre-treatment MRI-defined stage of rectal cancer.

METHODS

A retrospectively analysis was conducted in non-metastatic rectal cancer patients who received radical tumor resection without neoadjuvant treatment during 2007-2015 in the Sixth Affiliated Hospital of Sun Yat-sen University. Clinical T stage and N stage defined by pelvic enhanced MRI and pathological stage were compared and patients were subdivided into accurate-staging, over-staging and under-staging subgroups. Logistic regressions were used to explore risk factors for over-staging or under-staging.

RESULTS

Five hundred fifty-one cases of patients were collected. Among them, 109 cases (19.4%) of patients were over-T-staged and 50 cases (8.9%) were under-T-staged, while 78 cases (13.9%) were over-N-staged and 75 cases (13.3%) were under-N-staged. Logistic regression suggested that pre-operative bowel obstruction was risk factor for over-T-staging (OR = 3.120, 95%CI: 1.662-5.857, P < 0.001) as well as over-N-staging (OR = 3.494, 95%CI: 1.797-6.794, P < 0.001), while mucinous adenocarcinoma was a risk factor for under-N-staging (OR = 4.049, 95%CI: 1.876-8.772, P < 0.001). Patients with larger tumor size were at lower risk for over-T-staging (OR = 0.837, 95%CI: 0.717-0.976, P = 0.024) and higher risk for over-N-staging (OR = 1.434, 95%CI: 1.223-1.680, P < 0.001).

CONCLUSION

Bowel obstruction, mucinous adenocarcinoma and tumor size might have impact on the pre-operative MRI T staging or N staging of rectal cancer. Our results reminded clinicians to assess clinical stage individually in such rectal cancer patients.

The Correlation Between Computed Tomography Volumetry and Prognosis of Advanced Gastric Cancer Treated with Neoadjuvant Chemotherapy

Purpose

To investigate the feasibility and utility of computer tomography (CT) volumetry in evaluating the tumor response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) patients.

Patients and Methods

One hundred and seventeen Patients with AGC who received NAC followed by R0 resection between January 2006 and December 2012 were included. Tumor volumes were quantified using OsiriX software. The volume reduction rate (VRR) was calculated as follows: VRR = [(pre-chemotherapy total volume) − (post-chemotherapy total volume)]/(pre-chemotherapy total volume) × 100%. The optimal cut-off VRR for differentiating favorable from unfavorable prognosis was determined by receiver operating characteristic (ROC) analysis. Overall survival was calculated using Kaplan-Meier analysis and values were compared using the Log-rank test. Multivariate analysis was determined by the Cox proportional regression model.

Results

The optimal cut-off VRR was 31.95% according to ROC analysis, with a sensitivity of 70.4% and a specificity of 71.7%. Based on the cut-off VRR, patients were divided into the VRR-High (VRR ≥ 31.95%, n = 63) and VRR-Low (VRR < 31.95%, n = 54) groups. The VRR-Low group exhibited a worse prognosis than that of the VRR-High group (HR, 2.85; 95% CI, 1.69–4.82, P < 0.001), with 3-year survival rates of 40.7% and 79.4%, and 5-year survival rates of 31.5% and 63.5%, respectively.

Conclusion

CT volumetry is a feasible and reliable method for assessing the tumor response to NAC in patients with AGC.

A Case with Rectal Cancer Relapses After Clinical Complete Remission Following Neoadjuvant Chemoradiotherapy

Despite advancements in diagnosis and therapy, relapse of rectal cancer after clinical complete remission (cCR) remains a frequent event. The key factors influencing the treatment strategy for the management of patients achieving cCR following neoadjuvant chemoradiotherapy (Neo-CRT) remain to be identified. We present the case of a 64-year-old man with rectal cancer. The patient was initially admitted to the hospital in September 2011 with a 3-month history of change in his stools. Following his re-hospitalization in November 2011, a biopsy specimen of the neoplasm suggested the presence of rectal adenocarcinoma; laboratory investigations also revealed elevated levels of carcinoembryonic antigens (CEA; carbohydrate antigen 199) in the serum. Subsequently, the patient received Neo-CRT, as well as symptomatic and supportive treatment. The level of serum CEA returned to normal, without signs of swollen lymph nodes in the pelvic cavity. The patient was diagnosed with rectal cancer based on the elevated level of serum CEA, colonoscopy, and contrast-enhanced magnetic resonance imaging. He relapsed 4 months after cCR following Neo-CRT and underwent laparoscopic Miles’ surgery in April 2013. The relapse may have been mainly attributed to residual tumor cells. This case report and literature review may contribute to the clinical recognition of treatment for patients with rectal cancer achieving cCR following Neo-CRT.

Baseline and early 3D-CUBE volume reconstruction of locally advanced rectal cancer to predict tumor response after neoadjuvant chemotherapy

PURPOSE

To explore whether volumetric measurements of 3D-CUBE sequences based on baseline and early treatment time can predict neoadjuvent chemotherapy (NCT) efficacy of locally advanced rectal cancer (LARC).

MATERIAL AND METHOD

73 patients with LARC were enrolled from February 2014 to January 2018. All patients underwent MRIs during the baseline period before NCT (BL-NCT) and the first month of NCT (FM-NCT), and tumor volume (TV) was measured using 3D-CUBE, and tumor volume reduction (TVR) and tumor volume reduction rate (TVRR) were calculated. In addition, tumor invasion depth, tumor maximal length, range of tumor involvement in the circumference of intestinal lumen and distance from inferior part of tumor to the anal verge were measured using baseline high-spatial-resolution T2-weighted MRIs. All patients were categorized into sensitive and insensitive groups based on post-surgical pathology after completion of the full courses of NCT. The receiver operating characteristic (ROC) curve was used to analyze the value of different MRI parameters in predicting efficacy of NCT.

RESULTS

Statistically significant differences in TV of BL-NCT, TVR and TVRR from BL-NCT to FM-NCT were detected between sensitive and insensitive groups (P < 0.05, respectively). The areas under the curves (AUC) of ROC of TVR and TVRR in predicting efficacy of NCT (0.890 [95% CI, 0.795∼0.951], 0.839 [95% CI, 0.735∼0.915]) were significantly better than that of TV (0.660 [95% CI, 0.540∼0.767]) (P < 0.05, respectively).

CONCLUSION

Reconstruction of 3D-CUBE volume in the first month of NCT is necessary, and both TVR and TVRR can be used as early predictors of NCT efficacy.

Locally advanced rectal cancer: qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging in restaging after neoadjuvant chemo-radiotherapy

PURPOSE

To determine the added value of qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging (DWI) in locally advanced rectal cancer (LARC) restaging after neoadjuvant chemo-radiotherapy (CRT).

MATERIALS AND METHODS

A retrospective study was performed of 21 patients with LARC treated with CRT. All patients were evaluated with 1.5 T conventional magnetic resonance imaging (MRI) and DWI (0-1000 s/mm²) before starting therapy and after neoadjuvant CRT. All included patients underwent surgery after CRT: the histopathological evaluation of surgical specimens represented the reference standard for local staging after neoadjuvant therapy. The qualitative analysis was carried out by two operators in consensus, who reviewed the conventional MR image set [T1-weighted and T2-weighted morphological sequences + dynamic contrast-enhanced sequences (DCE)] and the combined set of conventional and DW images. For the quantitative analysis, the apparent diffusion coefficient (ADC) values were measured at each examination. For each lesion, the mean ADC value (ADCpre and ADCpost) and the ΔADC (ADCpost - ADCpre) were calculated, and values of the three groups of response [complete response (pCR), partial response (pPR), stable disease (pSD)] were compared.

RESULTS

In LARC restaging, conventional MRI showed a sensitivity of 80% and a specificity of 50%, with a total diagnostic capacity of 71.40%, while by adding DWI sensitivity increased to 100%, specificity to 67%, and total diagnostic capacity to 90.40%. ΔADC correlates with treatment response and a cutoff of 1.35 × 10^-3 mm²/s predicts the pCR with a sensitivity of 93.3% and a specificity of 83.3%.

CONCLUSIONS

Adding DWI to conventional sequences may improve MRI capability to evaluate tumor response to CRT. The quantitative DWI assessment is promising, but larger studies are required.

Comparison between MRI and pathology in the assessment of tumour regression grade in rectal cancer

Background:

Limited data exist regarding the correlation between MRI tumour regression grade (mrTRG) and pathological TRG (pTRG) in rectal cancer.

Methods:

mrTRG and pTRG were compared in rectal cancer patients from two phase II trials (EXPERT and EXPERT-C). The agreement between radiologist and pathologist was assessed with the weighted κ test while the Kaplan–Meier method was used to estimate survival outcomes.

Results:

One hundred ninety-one patients were included. Median time from completion of neoadjuvant treatment to pre-operative MRI and surgery was 4.1 weeks (interquartile range (IQR): 3.7–4.7) and 6.6 weeks (IQR: 5.9–7.6), respectively. Fair agreement was found between mrTRG and pTRG when regression was classified according to standard five-tier systems (κ=0.24) or modified three-tier systems (κ=0.25). Sensitivity and specificity of mrTRG 1–2 (complete/good radiological regression) for the prediction of pathological complete response was 74.4% (95% CI: 58.8–86.5) and 62.8% (95% CI: 54.5–70.6), respectively. Survival outcomes of patients with intermediate pathological regression (pTRG 2) were numerically better if complete/good regression was also observed on imaging (mrTRG 1–2) compared to poor regression (mrTRG 3–5) (5-year recurrence-free survival 76.9% vs 65.9%, P=0.18; 5-year overall survival 80.6% vs 68.8%, P=0.22).

Conclusions:

The agreement between mrTRG and pTRG is low and mrTRG cannot be used as a surrogate of pTRG. Further studies are warranted to assess the ability of mrTRG to identify pathological complete responders for the adoption of non-operative management strategies and to provide complementary prognostic information to pTRG for better risk-stratification after surgery.

Prognostic influence of histopathological regression patterns in rectal adenocarcinoma receiving neoadjuvant therapy

BACKGROUND

Neoadjuvant chemoradiation therapy (CRT) is an important management strategy in rectal carcinoma. Different systems grading response have shown varying prognostic influence.

METHODS

To analyze the prognostic influence of pathological response in a series of 183 patients with rectal carcinoma receiving neoadjuvant therapy. To determine the prognostic significance of the histopathological patterns of response.

RESULTS

A total of 183 patients from two hospitals. The concordance rate between pathologists was good. In total, 18% of the patients showed grade 0 (complete response), 31.7% grade 1, 19.2% grade 2 and 31.1% grade 3 regression. T down-staging was found in 51.9% of the cases. 46 patients recurred and 18 died of disease (median follow-up time: 39 months). We found a statistically significant association between pathological response and pT stage and down-staging. Inflammatory reaction in the tumor bed was significantly associated to regression and prognosis. Cox's multivariate analysis of survival revealed that down-staging and presence of mucin pools in the tumor bed behaved as significant predictors of recurrence and regression grade and mucin pools as significant predictors of survival.

CONCLUSIONS

Pathological response is an important surrogate marker of prognosis in some large series, but results are varying. There are many systems to grade regression and this makes it difficult to compare the results by different groups. It is important to report the specific pattern of response, for some of them may have prognostic relevance. We feel there is an urgent need to develop standarized protocols and employ a universal regression scheme if we intend to use this factor to guide therapy.

The Prognostic Impact of Tumor Volume in Patients with Clinical Stage IA Non-Small Cell Lung Cancer

INTRODUCTION

Tumor volume promises to be an important factor for predicting the prognosis of patients with non-small cell lung cancer (NSCLC).

METHODS

A total of 255 patients who underwent curative surgical resection for clinical stage IA NSCLC were included. We performed semiautomated measurement of the whole tumor volume and the volume of the solid part (referred to as the solid part volume) from a volumetric analysis of chest three-dimensional computed tomography scans using the SYNAPSE VINCENT imaging software program (Fujifilm Medical, Tokyo, Japan). We evaluated the relationships among tumor size, tumor volume, and survival.

RESULTS

The mean whole tumor size, the ratio of the maximum diameter of consolidation to the maximum tumor diameter (CTR), the whole tumor volume, and the solid part volume were 20 mm (range 0-30 mm), 0.84 (range 0-1.0), 3080 mm(3) (range 123-17509 mm(3)), and 2032 mm(3) (0-12466 mm(3)), respectively. The receiver operating characteristic area under the curve for the whole tumor size, CTR, whole tumor volume, and solid part volume used to identify recurrence were 0.60, 0.68, 0.58, and 0.69, respectively. A univariate analysis revealed that the whole tumor size, CTR, whole tumor volume, and solid part volume were associated with disease-free survival (DFS). A multivariate analysis of these factors identified the solid part volume to be the only independent factor for the prediction of DFS.

CONCLUSIONS

The whole tumor volume and the solid part volume were associated with DFS. In particular, the solid part volume was a very useful factor for predicting prognosis in clinical stage IA NSCLC.