Inter-Observer and Intra-Observer Variability in Gross Tumor Volume Delineation of Primary Esophageal Carcinomas Based on Different Combinations of Diagnostic Multimodal Images

Inter-observer variation in gross tumour volume delineation of oesophageal cancer on MR, CT and PET/CT

BACKGROUND

The aim of our study was to assess the inter-observer variability in delineation of the gross tumour volume (GTV) of oesophageal cancer on magnetic resonance (MR) in comparison to computed tomography (CT) and positron emission tomography and CT (PET/CT).

PATIENTS AND METHODS

Twenty-three consecutive patients with oesophageal cancer treated with chemo-radiotherapy were enrolled. All patients had PET/CT and MR imaging in treatment position. Five observers independently delineated the GTV on CT alone, MR alone, CT with co-registered MR, PET/CT alone and MR with co-registered PET/CT. Volumes of GTV were measured per patient and imaging modality. Inter-observer agreement, expressed in generalized conformity index (CIgen), volumetric conformity index (VCI), planar conformity index (PCI) and inter-delineation distance (IDD) were calculated per patient and imaging modality. Linear mixed models were used for statistical analysis.

RESULTS

GTV volume was significantly lower on MR (33.03 cm3) compared to CT (37.1 cm3; p = 0.002) and on PET/CT MR (35.2 cm3; p = 0.018) compared to PET/CT (39.1 cm3). The CIgen was lowest on CT (0.56) and highest on PET/CT MR (0.67). The difference in CIgen between MR (0.61) and CT was borderline significant (p = 0.048). The VCI was significantly higher on MR (0.71; p = 0.007) and on CT MR (0.71; p = 0.004) compared to CT (0.67). The PCI was significantly higher on CT MR (0.67; p = 0.031) compared to CT (0.64). The largest differences were observed in the cranio-caudal direction.

CONCLUSIONS

The highest inter-observer agreement was found for PET/CT MR and the lowest for CT. MR could reduce the difference in delineation between observers and provide additional information about the local extent of the tumour.

Head and neck automatic multi-organ segmentation on Dual-Energy Computed Tomography

Background and purpose

Deep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT.

Material and methods

Different types of DECT images from seventy four head-and-neck (HN) patients were retrieved, including polyenergetic images at different voltages [80 kV reconstructed with a kernel corresponding to the commercial algorithm DirectDensity™ (PEI80-DD), 80 kV (PEI80), 120 kV-mixed (PEI120)] and a virtual-monoenergetic image at 40 keV (VMI40). Delineations used for treatment planning were considered as ground truth (GT) and were compared with the auto-segmentations performed on the 4 DECT images. A blinded qualitative evaluation of 3 structures (thyroid, left parotid, left nodes level II) was carried out. Performance metrics were calculated for thirteen HN structures to evaluate the auto-contours including dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and mean surface distance (MSD).

Results

We observed a high rate of low scores for PEI80-DD and VMI40 auto-segmentations on the thyroid and for GT and VMI40 contours on the nodes level II. All images received excellent scores for the parotid glands. The metrics comparison between GT and auto-segmented contours revealed that PEI80-DD had the highest DSC scores, significantly outperforming other reconstructed images for all organs (p < 0.05).

Conclusions

The results indicate that the auto-contouring system cannot generalize to images derived from DECT acquisition. It is therefore crucial to identify which organs benefit from these acquisitions to adapt the training datasets accordingly.

Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma

BACKGROUND

Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival.

METHODS

Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality.

RESULTS

A total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p = .04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p > .05). Test sensitivity of 90% was achieved by ClinRad and Stage models only.

CONCLUSIONS

Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival.

CLINICAL RELEVANCE STATEMENT

Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own.

KEY POINTS

• Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features-GLCM_Correlation and GLCM_Contrast-contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival.

The Role of MRI and PET/CT in Radiotherapy Target Volume Determination in Gastrointestinal Cancers-Review of the Literature

Positron emission tomography with computed tomography (PET/CT) and magnetic resonance imaging (MRI) could improve accuracy in target volume determination for gastrointestinal cancers. A systematic search of the PubMed database was performed, focusing on studies published within the last 20 years. Articles were considered eligible for the review if they included patients with anal canal, esophageal, rectal or pancreatic cancer, as well as PET/CT or MRI for radiotherapy treatment planning, and if they reported interobserver variability or changes in treatment planning volume due to different imaging modalities or correlation between the imaging modality and histopathologic specimen. The search of the literature retrieved 1396 articles. We retrieved six articles from an additional search of the reference lists of related articles. Forty-one studies were included in the final review. PET/CT seems indispensable for target volume determination of pathological lymph nodes in esophageal and anal canal cancer. MRI seems appropriate for the delineation of primary tumors in the pelvis as rectal and anal canal cancer. Delineation of the target volumes for radiotherapy of pancreatic cancer remains challenging, and additional studies are needed.