Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer.

Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.

Utility of percutaneous lung biopsy for diagnosing filamentous fungal infections in hematologic malignancies.

BACKGROUND AND OBJECTIVES: The incidence of invasive filamentous fungal infections in hematologic patients is increasing as a consequence of high dose chemotherapy and bone marrow transplant procedures. Mortality is usually very high. The diagnosis is often difficult and yet a fast, accurate diagnosis is of fundamental importance for treating the infection and planning subsequent management of the hematologic disease. We evaluated the sensitivity of computed tomography (CT)-guided percutaneous biopsy in diagnosing pulmonary fungal infections. DESIGN AND METHODS: Between 1997 and 2002 we performed 17 CT-guided percutaneous transthoracic lung biopsies in 17 hematologic patients with suspected filamentous fungi infection with negative BAL, to obtain a certain diagnosis and to know what species of fungi was responsible for infection. In all cases suspected mycosis began during the post-chemotherapy aplastic period. Patients were receiving antifungal therapy at the time of all biopsies. When the platelet count rose above 50 x 10(9)/L, CT-guided percutaneous lung biopsy with fine-needle aspiration for cytology was performed. RESULTS: Twelve of 17 patients had histologic confirmation of the fungal infection (70.5%), 8 with Aspergillus spp. 4 with Mucorales spp. Biopsies provided non-specific results in 4 cases; in 2 of these cases, clinical course and response to therapy confirmed the diagnosis of mycosis; in the last case bronchoalveolar carcinoma was found as a new diagnosis. Cultures were positive in only 6 cases, all for Aspergillus spp. The sensitivity of CT-guided percutaneous lung biopsy was 70.6% and its positive predictive value (PPV) was 100%. This procedure provided an immediate diagnosis and only one side-effect (1 pneumothorax, without complications). INTERPRETATION AND CONCLUSIONS: Histologic discrimination between aspergillosis and mucormycosis is very important for deciding secondary prophylaxis during transplant procedures, because Mucor is usually resistant to azoles.