Standardized methods provide contemporary international benchmarks for reporting outcomes after esophagectomy.

The stage of esophageal or esophagogastric junction adenocarcinoma after neoadjuvant chemotherapy determines prognosis rather than the clinical stage before neoadjuvant chemotherapy, indicating the importance of focusing on postchemotherapy staging to more accurately predict outcome and eligibility for surgery. Patients who are downstaged by neoadjuvant chemotherapy benefit from reduced rates of local and systemic recurrence.

• Changes in CT body composition occur after neoadjuvant chemotherapy in oesophageal cancer. • Sarcopenia was more prevalent after neoadjuvant chemotherapy. • Fat mass, fat-free mass and weight decreased after neoadjuvant chemotherapy. • Changes in body composition were associated with CRM positivity. • Changes in body composition did not affect perioperative complications and survival.

SUMMARY To address uncertainty of whether clinical stage groupings (cTNM) for esophageal cancer share prognostic implications with pathologic groupings after esophagectomy alone (pTNM), we report data—simple descriptions of patient characteristics, cancer categories, and non-risk-adjusted survival—for clinically staged patients from the Worldwide Esophageal Cancer Collaboration (WECC). Thirty-three institutions from six continents submitted data using variables with standard definitions: demographics, comorbidities, clinical cancer categories, and all-cause mortality from first management decision. Of 22,123 clinically staged patients, 8,156 had squamous cell carcinoma, 13,814 adenocarcinoma, 116 adenosquamous carcinoma, and 37 undifferentiated carcinoma. Patients were older (62 years) men (80%) with normal body mass index (18.5–25 mg/kg2, 47%), little weight loss (2.4 ± 7.8 kg), 0–1 ECOG performance status (67%), and history of smoking (67%). Cancers were cT1 (12%), cT2 (22%), cT3 (56%), cNO (44%), cMO (95%), and cG2–G3 (89%); most involved the distal esophagus (73%). Non-risk-adjusted survival for squamous cell carcinoma was not distinctive for early cT or cN; for adenocarcinoma, it was distinctive for early versus advanced cT and for cNO versus cN+. Patients with early cancers had worse survival and those with advanced cancers better survival than expected from equivalent pathologic categories based on prior WECC pathologic data. Thus, clinical and pathologic categories do not share prognostic implications. This makes clinically based treatment decisions difficult and pre-treatment prognostication inaccurate. These data will be the basis for the 8th edition cancer staging manuals following risk adjustment for patient characteristics, cancer categories, and treatment characteristics and should direct 9th edition data collection.

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

The object of this article is to assess current staging accuracies for individual modalities and to investigate the influence of the multidisciplinary team (MDT) on clinical staging accuracies and treatment selection for patients with gastro-esophageal cancer. Patients newly diagnosed with gastric or esophageal cancer and who were deemed suitable for surgical resection by the MDT were studied. Patients were staged with a combination of computerized tomography (CT), endoscopic ultrasound (EUS) and laparoscopic ultrasound (LUS). Additionally, the MDT determined an overall clinical stage for each patient after discussion at the MDT meeting. Treatments were selected according to this final clinical stage. Final histopathological staging (pTNM) was available for all patients and was used as the gold standard for determining staging accuracy. Suitability of treatment selection was assessed once final pTNM was available. One hundred and eighteen patients were studied. Endoscopic ultrasound was the most accurate individual staging modality for the loco-regional assessment of esophageal tumors (T stage accuracy 78%, N stage accuracy 70%). Laparoscopic ultrasound was the most accurate modality in T staging of gastric cancers (91%). The MDT stage was more accurate than each individual staging modality for T and N staging for both gastric and esophageal cancers (accuracy range: 88-89%) and was better for the assessment of nodal disease than each individual modality (CT P < 0.001, EUS P < 0.01, LUS P < 0.01). Overall staging accuracy as determined at the MDT meeting was increased and resulted in only 2/118 (2%) patients being under-treated. The MDT significantly improves staging accuracy for gastro-esophageal cancer and ensures that correct management decisions are made for the highest number of individual patients.

This study indicated that the extent of lymphadenectomy during surgery for esophageal cancer might not influence 5-year all-cause or disease-specific survival. These results challenge current clinical guidelines.

Lymph node regression is a strong prognostic factor and may be more important than response in the primary tumour.