Personalized four-category staging for predicting prognosis in patients with small bowel Adenocarcinoma: an international development and validation study

Small bowel adenocarcinoma of the jejunum detected by double contrast enhanced ultrasound: a case report of a novel ultrasound modality

Background

Small Bowel Adenocarcinoma (SBA) is rare, occult and life-threatening malignancy in digestive system. Given low incidence and nonspecific symptoms, SBA is frequently detected in later stages. Double contrast enhanced ultrasound (DCEUS) is an innovative imaging technique applied to visualize the gastrointestinal tract, merging intravenous contrast-enhanced ultrasound with oral contrast-enhanced ultrasound. In this case, DCEUS was utilized and successfully detected an SBA of the jejunum.

Case presentation

A Chinese woman, aged 64, sought consultation in the gastroenterology department at our hospital, reporting symptoms of abdominal pain. Three months before entering the hospital, she underwent gastroscopy and colonoscopy which suggested chronic gastritis, and she was treated with oral drugs. However, her symptoms were not relieved, and even worsened. To further investigate, DCEUS was performed. The oral contrast agent dilated the luminal space of the upper gastrointestinal tract, resolving the hindrance caused by gas in the gastrointestinal tract and creating an acoustic window for scanning. Through this acoustic window, oral agent contrast-enhanced ultrasound (OA-CEUS) revealed a localized thickening of jejunal intestinal wall measuring 4x3 cm. Following intravenous injection of ultrasound contrast agent, the jejunal lesion exhibited faster enhancement and heterogeneous hyper-enhancement. Finally, the patient underwent jejunal tumor resection. Pathological examination revealed a jejunal adenocarcinoma.

Conclusion

The timely diagnosis of SBA can be challenging. DCEUS may have the potential to contribute to diagnosis and detailed evaluation of SBA, particularly in cases involving jejunum. Further researches are needed to fully explore the benefits of DCEUS in the standard diagnostic approach for small bowel diseases.

Pathological-Features-Modified TNM Staging System Improves Prognostic Accuracy for Rectal Cancer

BACKGROUND

Variations in survival outcomes are observed in the eighth edition of the American Joint Committee on Cancer TNM staging system.

OBJECTIVE

Machine learning ensemble methods were used to develop and evaluate the effectiveness of a pathological-features-modified TNM staging system in predicting survival for patients with rectal cancer by use of commonly reported pathological features, such as histological grade, tumor deposits, and perineural invasion, to improve the prognostic accuracy.

DESIGN

This was a retrospective population-based study.

SETTINGS

Data were assessed from the database of the Surveillance, Epidemiology, and End Results Program.

PATIENTS

The study cohort comprised 14,468 patients with rectal cancer diagnosed between 2010 and 2015. The development cohort included those who underwent surgery as the primary treatment, whereas patients who received neoadjuvant therapy were assigned to the validation cohort.

MAIN OUTCOME MEASURES

The primary outcome measures included cumulative rectal cancer survival, adjusted HRs, and both calibration and discrimination statistics to evaluate model performance and internal validation.

RESULTS

Multivariable Cox regression analysis identified all 3 pathological features as prognostic factors, after which patients were categorized into 4 pathological groups based on the number of pathological features (ie, 0, 1, 2, and 3). Distinct survival differences were observed among the groups, especially with patients with stage III rectal cancer. The proposed pathological-features-modified TNM staging outperformed the TNM staging in both the development and validation cohorts.

LIMITATIONS

Retrospective in design and lack of external validation.

CONCLUSIONS

The proposed pathological-features-modified TNM staging could complement the current TNM staging by improving the accuracy of survival estimation of patients with rectal cancer. See Video Abstract .

EL SISTEMA DE ESTADIFICACIN TNM CON CARACTERSTICAS PATOLGICAS MODIFICADO MEJORA LA PRECISIN DEL PRONSTICO DEL CNCER DE RECTO

ANTECEDENTES:Se observan variaciones en los resultados de supervivencia en el sistema de estadificación TNM del Comité Conjunto Americano del Cáncer 8º ediciónOBJETIVO:Se utilizaron métodos conjuntos de aprendizaje automático para desarrollar y evaluar la eficacia de un sistema de estadificación con características patológicas modificadas de tumores, ganglios y metástasis para predecir la supervivencia de pacientes con cáncer de recto, utilizando algunas características patológicas comúnmente informadas, como el grado histológico, depósitos tumorales e invasión perineural, para mejorar la precisión del pronóstico.DISEÑO:Este fue un estudio retrospectivo de base poblacional.ENTERNO CLINICO:Se recuperaron y evaluaron datos de la base de datos de Vigilancia, Epidemiología y Resultados Finales.PACIENTES:La cohorte del estudio estuvo compuesta por 14,468 pacientes con cáncer de recto diagnosticados entre 2010 y 2015. La cohorte de desarrollo incluyó a aquellos que se sometieron a cirugía como tratamiento primario, mientras que los pacientes que recibieron terapia neoadyuvante fueron asignados a la cohorte de validación.PRINCIPALES MEDIDAS DE RESULTADO:Las medidas de resultado primarias incluyeron supervivencia acumulada del cáncer de recto, índices de riesgo ajustados y estadísticas de calibración y discriminación para evaluar el rendimiento del modelo y la validación interna.RESULTADOS:El análisis de regresión multivariable de Cox identificó las tres características patológicas como factores pronósticos, después de lo cual los pacientes se clasificaron en cuatro grupos patológicos según el número de características patológicas (es decir, 0, 1, 2 y 3). Se observaron distintas diferencias en la supervivencia entre los grupos, especialmente en los pacientes en estadio III. La estadificación propuesta con características patológicas modificadas de tumores-ganglios-metástasis superó a la estadificación TNM tanto en las cohortes de desarrollo como en las de validación.LIMITACIONES:Diseño retrospectivo y falta de validación externa.CONCLUSIONES:La estadificación propuesta con características patológicas modificadas de tumores-ganglios-metástasis podría complementar la estadificación TNM actual al mejorar la precisión de la estimación de supervivencia de los pacientes con cáncer de recto. (Traducción- Dr. Francisco M. Abarca-Rendon ).

New perspectives on cancer clinical research in the era of big data and machine learning

In the 21st century, the development of medical science has entered the era of big data, and machine learning has become an essential tool for mining medical big data. The establishment of the SEER database has provided a wealth of epidemiological data for cancer clinical research, and the number of studies based on SEER and machine learning has been growing in recent years. This article reviews recent research based on SEER and machine learning and finds that the current focus of such studies is primarily on the development and validation of models using machine learning algorithms, with the main directions being lymph node metastasis prediction, distant metastasis prediction, and prognosis-related research. Compared to traditional models, machine learning algorithms have the advantage of stronger adaptability, but also suffer from disadvantages such as overfitting and poor interpretability, which need to be weighed in practical applications. At present, machine learning algorithms, as the foundation of artificial intelligence, have just begun to emerge in the field of cancer clinical research. The future development of oncology will enter a more precise era of cancer research, characterized by larger data, higher dimensions, and more frequent information exchange. Machine learning is bound to shine brightly in this field.

Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data

Background

The clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.

Methods

A total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.

Results

A nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.

Conclusion

The established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08249-x.